Renaming examples in apex-pdp

Renaming the examples as per what the documentation expects them.
Otherwise the documents won't work.

Change-Id: Ib9e30bf5a4cec0fec981372e1d9f3a0ee5d60f2f
Issue-ID: POLICY-861
Signed-off-by: ramverma <ram.krishna.verma@ericsson.com>

33 files changed, 13297 insertions, 0 deletions

diff --git a/examples/examples-adaptive/pom.xml b/examples/examples-adaptive/pom.xml
new file mode 100644
index 000000000..66f991e88
--- /dev/null
+++ b/examples/examples-adaptive/pom.xml
@@ -0,0 +1,102 @@
+<!--
+  ============LICENSE_START=======================================================
+   Copyright (C) 2018 Ericsson. All rights reserved.
+  ================================================================================
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+
+  SPDX-License-Identifier: Apache-2.0
+  ============LICENSE_END=========================================================
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <parent>
+        <groupId>org.onap.policy.apex-pdp.examples</groupId>
+        <artifactId>examples</artifactId>
+        <version>2.0.0-SNAPSHOT</version>
+    </parent>
+
+    <artifactId>examples-adaptive</artifactId>
+    <name>${project.artifactId}</name>
+    <description>Specific code for the Apex Adaptive Example</description>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.onap.policy.apex-pdp.model</groupId>
+            <artifactId>policy-model</artifactId>
+            <version>${project.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>org.onap.policy.apex-pdp.core</groupId>
+            <artifactId>core-engine</artifactId>
+            <version>${project.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>org.onap.policy.apex-pdp.core</groupId>
+            <artifactId>core-infrastructure</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.onap.policy.apex-pdp.plugins.plugins-executor</groupId>
+            <artifactId>plugins-executor-mvel</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.onap.policy.apex-pdp.plugins.plugins-executor</groupId>
+            <artifactId>plugins-executor-java</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.commons</groupId>
+            <artifactId>commons-math3</artifactId>
+            <version>3.2</version>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.derby</groupId>
+            <artifactId>derby</artifactId>
+            <version>${version.derby}</version>
+            <scope>test</scope>
+        </dependency>
+    </dependencies>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.codehaus.mojo</groupId>
+                <artifactId>exec-maven-plugin</artifactId>
+                <executions>
+                    <execution>
+                        <id>generate-models</id>
+                        <phase>process-classes</phase>
+                        <goals>
+                            <goal>exec</goal>
+                        </goals>
+                        <configuration>
+                            <executable>java</executable>
+                            <arguments>
+                                <argument>-classpath</argument>
+                                <!-- automatically creates the classpath using all project dependencies, also adding the project build directory -->
+                                <classpath />
+                                <argument>org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelSaver</argument>
+                                <argument>${project.build.directory}/classes/examples/models/Adaptive</argument>
+                            </arguments>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+        </plugins>
+    </build>
+</project>
\ No newline at end of file
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java
new file mode 100644
index 000000000..f574c74ec
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java
@@ -0,0 +1,243 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.concepts;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.LinkedList;
+import java.util.List;
+
+/**
+ * The Class AnomalyDetection is used as a Java context for Adaptive anomaly detection in the adaptive domain.
+ */
+public class AnomalyDetection implements Serializable {
+    private static final long serialVersionUID = -823013127095523727L;
+
+    private static final int HASH_PRIME_1 = 31;
+    private static final int HASH_PRIME_2 = 1231;
+    private static final int HASH_PRIME_3 = 1237;
+
+    private boolean firstRound = true;
+    private int frequency = 0;
+
+    /**
+     * The Constructor creates an AnomalyDetection instance.
+     */
+    public AnomalyDetection() {
+        firstRound = true;
+        frequency = 0;
+    }
+
+    private List<Double> anomalyScores = new LinkedList<>();
+    private List<Double> frequencyForecasted;
+
+    /**
+     * Checks if the AnomalyDetection instance is initialized.
+     *
+     * @return true, if the AnomalyDetection instance is initialized
+     */
+    public boolean isInitialized() {
+        return (frequencyForecasted != null);
+    }
+
+    /**
+     * Initializes the AnomalyDetection instance.
+     *
+     * @param incomingFrequency the frequency
+     */
+    public void init(final int incomingFrequency) {
+        frequencyForecasted = new ArrayList<>(incomingFrequency);
+        for (int i = 0; i < incomingFrequency; i++) {
+            frequencyForecasted.add(null);
+        }
+    }
+
+    /**
+     * Indicates if this is the first round of the algorithm.
+     *
+     * @return true if this is the first round of the algorithm
+     */
+    public boolean getFirstRound() {
+        return firstRound;
+    }
+
+    /**
+     * Sets the first round indicator of the algorithm.
+     *
+     * @param firstRound the first round indicator of the algorithm
+     */
+    public void setFirstRound(final boolean firstRound) {
+        this.firstRound = firstRound;
+    }
+
+    /**
+     * Gets the frequency value of the algorithm.
+     *
+     * @return the frequency value of the algorithm
+     */
+    public int getFrequency() {
+        return frequency;
+    }
+
+    /**
+     * Sets the frequency value of the algorithm.
+     *
+     * @param frequency the frequency value of the algorithm
+     */
+    public void setFrequency(final int frequency) {
+        this.frequency = frequency;
+    }
+
+    /**
+     * Gets the anomaly score values of the algorithm.
+     *
+     * @return the anomaly score values of the algorithm
+     */
+    public List<Double> getAnomalyScores() {
+        return anomalyScores;
+    }
+
+    /**
+     * Sets the anomaly score values of the algorithm.
+     *
+     * @param anomalyScores the anomaly score values of the algorithm
+     */
+    public void setAnomalyScores(final LinkedList<Double> anomalyScores) {
+        this.anomalyScores = anomalyScores;
+    }
+
+    /**
+     * Check if the anomaly score values of the algorithm are set.
+     *
+     * @return true, if the anomaly score values of the algorithm are set
+     */
+    public boolean checkSetAnomalyScores() {
+        return ((anomalyScores != null) && (!anomalyScores.isEmpty()));
+    }
+
+    /**
+     * Unset the anomaly score values of the algorithm.
+     */
+    public void unsetAnomalyScores() {
+        anomalyScores = null;
+    }
+
+    /**
+     * Gets the frequency forecasted by the algorithm.
+     *
+     * @return the frequency forecasted by the algorithm
+     */
+    public List<Double> getFrequencyForecasted() {
+        return frequencyForecasted;
+    }
+
+    /**
+     * Sets the frequency forecasted by the algorithm.
+     *
+     * @param frequencyForecasted the frequency forecasted by the algorithm
+     */
+    public void setFrequencyForecasted(final List<Double> frequencyForecasted) {
+        this.frequencyForecasted = frequencyForecasted;
+    }
+
+    /**
+     * Check if the frequency forecasted by the algorithm is set.
+     *
+     * @return true, if the frequency forecasted by the algorithm is set
+     */
+    public boolean checkSetFrequencyForecasted() {
+        return ((frequencyForecasted != null) && (!frequencyForecasted.isEmpty()));
+    }
+
+    /**
+     * Unset the frequency forecasted by the algorithm.
+     */
+    public void unsetFrequencyForecasted() {
+        frequencyForecasted = null;
+    }
+
+    /*
+     * (non-Javadoc)
+     *
+     * @see java.lang.Object#toString()
+     */
+    @Override
+    public String toString() {
+        return "AnomalyDetection [firstRound=" + firstRound + ", frequency=" + frequency + ", anomalyScores="
+                + anomalyScores + ", frequencyForecasted=" + frequencyForecasted + "]";
+    }
+
+    /*
+     * (non-Javadoc)
+     *
+     * @see java.lang.Object#hashCode()
+     */
+    @Override
+    public int hashCode() {
+        final int prime = HASH_PRIME_1;
+        int result = 1;
+        result = prime * result + ((anomalyScores == null) ? 0 : anomalyScores.hashCode());
+        result = prime * result + (firstRound ? HASH_PRIME_2 : HASH_PRIME_3);
+        result = prime * result + frequency;
+        result = prime * result + ((frequencyForecasted == null) ? 0 : frequencyForecasted.hashCode());
+        return result;
+    }
+
+    /*
+     * (non-Javadoc)
+     *
+     * @see java.lang.Object#equals(java.lang.Object)
+     */
+    @Override
+    public boolean equals(final Object obj) {
+        if (this == obj) {
+            return true;
+        }
+        if (obj == null) {
+            return false;
+        }
+        if (getClass() != obj.getClass()) {
+            return false;
+        }
+        final AnomalyDetection other = (AnomalyDetection) obj;
+        if (anomalyScores == null) {
+            if (other.anomalyScores != null) {
+                return false;
+            }
+        } else if (!anomalyScores.equals(other.anomalyScores)) {
+            return false;
+        }
+        if (firstRound != other.firstRound) {
+            return false;
+        }
+        if (frequency != other.frequency) {
+            return false;
+        }
+        if (frequencyForecasted == null) {
+            if (other.frequencyForecasted != null) {
+                return false;
+            }
+        } else if (!frequencyForecasted.equals(other.frequencyForecasted)) {
+            return false;
+        }
+        return true;
+    }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java
new file mode 100644
index 000000000..f2e27725b
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java
@@ -0,0 +1,198 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.concepts;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * The Class AutoLearn is used as a Java context for Adaptive auto-learning of trends towards a fixed value in the
+ * adaptive domain.
+ */
+public class AutoLearn implements Serializable {
+    private static final long serialVersionUID = 3825970380434170754L;
+
+    private List<Double> avDiffs = null;
+
+    private List<Long> counts = null;
+
+    /**
+     * The Constructor creates an AutoLearn concept.
+     */
+    public AutoLearn() {}
+
+    /**
+     * Checks if the Autolearn instance is initialized.
+     *
+     * @return true, if the Autolearn instance is initialized
+     */
+    public boolean isInitialized() {
+        return (avDiffs != null && counts != null);
+    }
+
+    /**
+     * initializes the auto learning algorithm with the number of convergent variables to use.
+     *
+     * @param size the number of convergent variables to use
+     */
+    public void init(final int size) {
+        if (avDiffs == null || avDiffs.size() == 0) {
+            avDiffs = new ArrayList<>(size);
+            for (int i = 0; i < size; i++) {
+                avDiffs.add(i, Double.NaN);
+            }
+        }
+
+        if (counts == null || counts.size() == 0) {
+            counts = new ArrayList<>(size);
+            for (int i = 0; i < size; i++) {
+                counts.add(i, 0L);
+            }
+        }
+    }
+
+    /**
+     * Gets the average difference values of the algorithm.
+     *
+     * @return the average difference values of the algorithm
+     */
+    public List<Double> getAvDiffs() {
+        return avDiffs;
+    }
+
+    /**
+     * Sets the average difference values of the algorithm.
+     *
+     * @param avDiffs the average difference values of the algorithm
+     */
+    public void setAvDiffs(final List<Double> avDiffs) {
+        this.avDiffs = avDiffs;
+    }
+
+    /**
+     * Check if the average difference values of the algorithm are set.
+     *
+     * @return true, if check set av diffs
+     */
+    public boolean checkSetAvDiffs() {
+        return ((avDiffs != null) && (!avDiffs.isEmpty()));
+    }
+
+    /**
+     * Unset the average difference values of the algorithm.
+     */
+    public void unsetAvDiffs() {
+        avDiffs = null;
+    }
+
+    /**
+     * Gets the count values of the algorithm.
+     *
+     * @return the count values of the algorithm
+     */
+    public List<Long> getCounts() {
+        return counts;
+    }
+
+    /**
+     * Sets the count values of the algorithm.
+     *
+     * @param counts the count values of the algorithm
+     */
+    public void setCounts(final List<Long> counts) {
+        this.counts = counts;
+    }
+
+    /**
+     * Check if the count values of the algorithm are set.
+     *
+     * @return true, if the count values of the algorithm are set
+     */
+    public boolean checkSetCounts() {
+        return ((counts != null) && (!counts.isEmpty()));
+    }
+
+    /**
+     * Unset the count values of the algorithm.
+     */
+    public void unsetCounts() {
+        counts = null;
+    }
+
+    /*
+     * (non-Javadoc)
+     *
+     * @see java.lang.Object#toString()
+     */
+    @Override
+    public String toString() {
+        return "AutoLearn [avDiffs=" + avDiffs + ", counts=" + counts + "]";
+    }
+
+    /*
+     * (non-Javadoc)
+     *
+     * @see java.lang.Object#hashCode()
+     */
+    @Override
+    public int hashCode() {
+        final int prime = 31;
+        int result = 1;
+        result = prime * result + ((avDiffs == null) ? 0 : avDiffs.hashCode());
+        result = prime * result + ((counts == null) ? 0 : counts.hashCode());
+        return result;
+    }
+
+    /*
+     * (non-Javadoc)
+     *
+     * @see java.lang.Object#equals(java.lang.Object)
+     */
+    @Override
+    public boolean equals(final Object obj) {
+        if (this == obj) {
+            return true;
+        }
+        if (obj == null) {
+            return false;
+        }
+        if (getClass() != obj.getClass()) {
+            return false;
+        }
+        final AutoLearn other = (AutoLearn) obj;
+        if (avDiffs == null) {
+            if (other.avDiffs != null) {
+                return false;
+            }
+        } else if (!avDiffs.equals(other.avDiffs)) {
+            return false;
+        }
+        if (counts == null) {
+            if (other.counts != null) {
+                return false;
+            }
+        } else if (!counts.equals(other.counts)) {
+            return false;
+        }
+        return true;
+    }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/package-info.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/package-info.java
new file mode 100644
index 000000000..8561ed074
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Provides Java types that are used in the Adaptive domain.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive.concepts;
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelFactory.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelFactory.java
new file mode 100644
index 000000000..6242b1618
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelFactory.java
@@ -0,0 +1,558 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model;
+
+import org.onap.policy.apex.model.basicmodel.concepts.ApexRuntimeException;
+import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxKeyInformation;
+import org.onap.policy.apex.model.basicmodel.concepts.AxReferenceKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbum;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbums;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextSchema;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextSchemas;
+import org.onap.policy.apex.model.eventmodel.concepts.AxEvent;
+import org.onap.policy.apex.model.eventmodel.concepts.AxEvents;
+import org.onap.policy.apex.model.eventmodel.concepts.AxField;
+import org.onap.policy.apex.model.policymodel.concepts.AxLogicReader;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicies;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicy;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxState;
+import org.onap.policy.apex.model.policymodel.concepts.AxStateOutput;
+import org.onap.policy.apex.model.policymodel.concepts.AxStateTaskOutputType;
+import org.onap.policy.apex.model.policymodel.concepts.AxStateTaskReference;
+import org.onap.policy.apex.model.policymodel.concepts.AxTask;
+import org.onap.policy.apex.model.policymodel.concepts.AxTaskLogic;
+import org.onap.policy.apex.model.policymodel.concepts.AxTaskSelectionLogic;
+import org.onap.policy.apex.model.policymodel.concepts.AxTasks;
+import org.onap.policy.apex.model.policymodel.handling.PolicyLogicReader;
+
+/**
+ * The Class AdaptiveDomainModelFactory.
+ */
+public class AdaptiveDomainModelFactory {
+
+    /**
+     * Gets the anomaly detection policy model.
+     *
+     * @return the anomaly detection policy model
+     */
+    // CHECKSTYLE:OFF: checkstyle:maximumMethodLength
+    public AxPolicyModel getAnomalyDetectionPolicyModel() {
+        // CHECKSTYLE:ON: checkstyle:maximumMethodLength
+        // Data types for event parameters
+        final AxContextSchema monitoredValue =
+                new AxContextSchema(new AxArtifactKey("MonitoredValue", "0.0.1"), "Java", "java.lang.Double");
+        final AxContextSchema iteration =
+                new AxContextSchema(new AxArtifactKey("Iteration", "0.0.1"), "Java", "java.lang.Integer");
+
+        final AxContextSchemas adContextSchemas = new AxContextSchemas(new AxArtifactKey("AADMDatatypes", "0.0.1"));
+        adContextSchemas.getSchemasMap().put(monitoredValue.getKey(), monitoredValue);
+        adContextSchemas.getSchemasMap().put(iteration.getKey(), iteration);
+
+        final AxEvent anomalyDetectionTriggerEvent =
+                new AxEvent(new AxArtifactKey("AnomalyDetectionTriggerEvent", "0.0.1"),
+                        "org.onap.policy.apex.examples.adaptive.events");
+        anomalyDetectionTriggerEvent.setSource("External");
+        anomalyDetectionTriggerEvent.setTarget("Match");
+        anomalyDetectionTriggerEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(anomalyDetectionTriggerEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        anomalyDetectionTriggerEvent.getParameterMap().put("Iteration", new AxField(
+                new AxReferenceKey(anomalyDetectionTriggerEvent.getKey(), "Iteration"), iteration.getKey()));
+
+        final AxEvent anomalyDetectionMatchEvent = new AxEvent(new AxArtifactKey("AnomalyDetectionMatchEvent", "0.0.1"),
+                "org.onap.policy.apex.examples.adaptive.events");
+        anomalyDetectionMatchEvent.setSource("Match");
+        anomalyDetectionMatchEvent.setTarget("Establish");
+        anomalyDetectionMatchEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(anomalyDetectionMatchEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        anomalyDetectionMatchEvent.getParameterMap().put("Iteration",
+                new AxField(new AxReferenceKey(anomalyDetectionMatchEvent.getKey(), "Iteration"), iteration.getKey()));
+
+        final AxEvent anomalyDetectionEstablishEvent =
+                new AxEvent(new AxArtifactKey("AnomalyDetectionEstablishEvent", "0.0.1"),
+                        "org.onap.policy.apex.examples.adaptive.events");
+        anomalyDetectionEstablishEvent.setSource("Establish");
+        anomalyDetectionEstablishEvent.setTarget("Decide");
+        anomalyDetectionEstablishEvent.getParameterMap().put("MonitoredValue",
+                new AxField(new AxReferenceKey(anomalyDetectionEstablishEvent.getKey(), "MonitoredValue"),
+                        monitoredValue.getKey()));
+        anomalyDetectionEstablishEvent.getParameterMap().put("Iteration", new AxField(
+                new AxReferenceKey(anomalyDetectionEstablishEvent.getKey(), "Iteration"), iteration.getKey()));
+
+        final AxEvent anomalyDetectionDecideEvent =
+                new AxEvent(new AxArtifactKey("AnomalyDetectionDecideEvent", "0.0.1"),
+                        "org.onap.policy.apex.examples.adaptive.events");
+        anomalyDetectionDecideEvent.setSource("Decide");
+        anomalyDetectionDecideEvent.setTarget("Act");
+        anomalyDetectionDecideEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(anomalyDetectionDecideEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        anomalyDetectionDecideEvent.getParameterMap().put("Iteration",
+                new AxField(new AxReferenceKey(anomalyDetectionDecideEvent.getKey(), "Iteration"), iteration.getKey()));
+
+        final AxEvent anomalyDetectionActEvent = new AxEvent(new AxArtifactKey("AnomalyDetectionActEvent", "0.0.1"),
+                "org.onap.policy.apex.examples.adaptive.events");
+        anomalyDetectionActEvent.setSource("Act");
+        anomalyDetectionActEvent.setTarget("External");
+        anomalyDetectionActEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(anomalyDetectionActEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        anomalyDetectionActEvent.getParameterMap().put("Iteration",
+                new AxField(new AxReferenceKey(anomalyDetectionActEvent.getKey(), "Iteration"), iteration.getKey()));
+
+        final AxEvents anomalyDetectionEvents = new AxEvents(new AxArtifactKey("AnomalyDetectionEvents", "0.0.1"));
+        anomalyDetectionEvents.getEventMap().put(anomalyDetectionTriggerEvent.getKey(), anomalyDetectionTriggerEvent);
+        anomalyDetectionEvents.getEventMap().put(anomalyDetectionMatchEvent.getKey(), anomalyDetectionMatchEvent);
+        anomalyDetectionEvents.getEventMap().put(anomalyDetectionEstablishEvent.getKey(),
+                anomalyDetectionEstablishEvent);
+        anomalyDetectionEvents.getEventMap().put(anomalyDetectionDecideEvent.getKey(), anomalyDetectionDecideEvent);
+        anomalyDetectionEvents.getEventMap().put(anomalyDetectionActEvent.getKey(), anomalyDetectionActEvent);
+
+        // Data types for context
+        final AxContextSchema anomalyDetection = new AxContextSchema(new AxArtifactKey("AnomalyDetection", "0.0.1"),
+                "Java", "org.onap.policy.apex.examples.adaptive.concepts.AnomalyDetection");
+        adContextSchemas.getSchemasMap().put(anomalyDetection.getKey(), anomalyDetection);
+
+        // One context map
+        final AxContextAlbum anomalyDetectionAlbum = new AxContextAlbum(
+                new AxArtifactKey("AnomalyDetectionAlbum", "0.0.1"), "APPLICATION", true, anomalyDetection.getKey());
+        final AxContextAlbums anomalyDetectionAlbums =
+                new AxContextAlbums(new AxArtifactKey("AnomalyDetectionAlbums", "0.0.1"));
+        anomalyDetectionAlbums.getAlbumsMap().put(anomalyDetectionAlbum.getKey(), anomalyDetectionAlbum);
+
+        // Tasks
+        final AxLogicReader logicReader =
+                new PolicyLogicReader().setLogicPackage(this.getClass().getPackage().getName())
+                        .setDefaultLogic("DefaultAnomalyDetectionTask_Logic");
+
+        final AxTask anomalyDetectionMatchTask = new AxTask(new AxArtifactKey("AnomalyDetectionMatchTask", "0.0.1"));
+        anomalyDetectionMatchTask.duplicateInputFields(anomalyDetectionTriggerEvent.getParameterMap());
+        anomalyDetectionMatchTask.duplicateOutputFields(anomalyDetectionMatchEvent.getParameterMap());
+        anomalyDetectionMatchTask
+                .setTaskLogic(new AxTaskLogic(anomalyDetectionMatchTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask anomalyDetectionEstablishTask =
+                new AxTask(new AxArtifactKey("AnomalyDetectionEstablishTask", "0.0.1"));
+        anomalyDetectionEstablishTask.duplicateInputFields(anomalyDetectionMatchEvent.getParameterMap());
+        anomalyDetectionEstablishTask.duplicateOutputFields(anomalyDetectionEstablishEvent.getParameterMap());
+        anomalyDetectionEstablishTask.setTaskLogic(
+                new AxTaskLogic(anomalyDetectionEstablishTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask anomalyDetectionDecideTask0 =
+                new AxTask(new AxArtifactKey("AnomalyDetectionDecideTask0", "0.0.1"));
+        anomalyDetectionDecideTask0.duplicateInputFields(anomalyDetectionEstablishEvent.getParameterMap());
+        anomalyDetectionDecideTask0.duplicateOutputFields(anomalyDetectionDecideEvent.getParameterMap());
+        anomalyDetectionDecideTask0
+                .setTaskLogic(new AxTaskLogic(anomalyDetectionDecideTask0.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask anomalyDetectionDecideTask1 =
+                new AxTask(new AxArtifactKey("AnomalyDetectionDecideTask1", "0.0.1"));
+        anomalyDetectionDecideTask1.duplicateInputFields(anomalyDetectionEstablishEvent.getParameterMap());
+        anomalyDetectionDecideTask1.duplicateOutputFields(anomalyDetectionDecideEvent.getParameterMap());
+        anomalyDetectionDecideTask1
+                .setTaskLogic(new AxTaskLogic(anomalyDetectionDecideTask1.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask anomalyDetectionDecideTask2 =
+                new AxTask(new AxArtifactKey("AnomalyDetectionDecideTask2", "0.0.1"));
+        anomalyDetectionDecideTask2.duplicateInputFields(anomalyDetectionEstablishEvent.getParameterMap());
+        anomalyDetectionDecideTask2.duplicateOutputFields(anomalyDetectionDecideEvent.getParameterMap());
+        anomalyDetectionDecideTask2
+                .setTaskLogic(new AxTaskLogic(anomalyDetectionDecideTask2.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask anomalyDetectionActTask = new AxTask(new AxArtifactKey("AnomalyDetectionActTask", "0.0.1"));
+        anomalyDetectionActTask.duplicateInputFields(anomalyDetectionDecideEvent.getParameterMap());
+        anomalyDetectionActTask.duplicateOutputFields(anomalyDetectionActEvent.getParameterMap());
+        anomalyDetectionActTask
+                .setTaskLogic(new AxTaskLogic(anomalyDetectionActTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTasks anomalyDetectionTasks = new AxTasks(new AxArtifactKey("AnomalyDetectionTasks", "0.0.1"));
+        anomalyDetectionTasks.getTaskMap().put(anomalyDetectionMatchTask.getKey(), anomalyDetectionMatchTask);
+        anomalyDetectionTasks.getTaskMap().put(anomalyDetectionEstablishTask.getKey(), anomalyDetectionEstablishTask);
+        anomalyDetectionTasks.getTaskMap().put(anomalyDetectionDecideTask0.getKey(), anomalyDetectionDecideTask0);
+        anomalyDetectionTasks.getTaskMap().put(anomalyDetectionDecideTask1.getKey(), anomalyDetectionDecideTask1);
+        anomalyDetectionTasks.getTaskMap().put(anomalyDetectionDecideTask2.getKey(), anomalyDetectionDecideTask2);
+        anomalyDetectionTasks.getTaskMap().put(anomalyDetectionActTask.getKey(), anomalyDetectionActTask);
+
+        // Policies
+        logicReader.setDefaultLogic("DefaultState_Logic");
+
+        final AxPolicy anomalyDetectionPolicy = new AxPolicy(new AxArtifactKey("AnomalyDetectionPolicy", "0.0.1"));
+        anomalyDetectionPolicy.setTemplate("MEDA");
+
+        final AxState anomalyDetectionActState =
+                new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Act"));
+        anomalyDetectionActState.setTrigger(anomalyDetectionDecideEvent.getKey());
+        final AxStateOutput adAct2Out = new AxStateOutput(anomalyDetectionActState.getKey(),
+                AxReferenceKey.getNullKey(), anomalyDetectionActEvent.getKey());
+        anomalyDetectionActState.getStateOutputs().put(adAct2Out.getKey().getLocalName(), adAct2Out);
+        anomalyDetectionActState.setTaskSelectionLogic(
+                new AxTaskSelectionLogic(anomalyDetectionActState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+        anomalyDetectionActState.setDefaultTask(anomalyDetectionActTask.getKey());
+        anomalyDetectionActState.getTaskReferences().put(anomalyDetectionActTask.getKey(),
+                new AxStateTaskReference(anomalyDetectionActState.getKey(), anomalyDetectionActTask.getKey(),
+                        AxStateTaskOutputType.DIRECT, adAct2Out.getKey()));
+
+        logicReader.setDefaultLogic(null);
+
+        final AxState anomalyDetectionDecideState =
+                new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Decide"));
+        anomalyDetectionDecideState.setTrigger(anomalyDetectionEstablishEvent.getKey());
+        final AxStateOutput adDec2Act = new AxStateOutput(anomalyDetectionDecideState.getKey(),
+                anomalyDetectionActState.getKey(), anomalyDetectionDecideEvent.getKey());
+        anomalyDetectionDecideState.getStateOutputs().put(adDec2Act.getKey().getLocalName(), adDec2Act);
+        anomalyDetectionDecideState.setTaskSelectionLogic(new AxTaskSelectionLogic(anomalyDetectionDecideState.getKey(),
+                "TaskSelectionLogic", "JAVA", logicReader));
+        anomalyDetectionDecideState.setDefaultTask(anomalyDetectionDecideTask0.getKey());
+        anomalyDetectionDecideState.getContextAlbumReferences().add(anomalyDetectionAlbum.getKey());
+        anomalyDetectionDecideState.getTaskReferences().put(anomalyDetectionDecideTask0.getKey(),
+                new AxStateTaskReference(anomalyDetectionDecideState.getKey(), anomalyDetectionDecideTask0.getKey(),
+                        AxStateTaskOutputType.DIRECT, adDec2Act.getKey()));
+        anomalyDetectionDecideState.getTaskReferences().put(anomalyDetectionDecideTask1.getKey(),
+                new AxStateTaskReference(anomalyDetectionDecideState.getKey(), anomalyDetectionDecideTask1.getKey(),
+                        AxStateTaskOutputType.DIRECT, adDec2Act.getKey()));
+        anomalyDetectionDecideState.getTaskReferences().put(anomalyDetectionDecideTask2.getKey(),
+                new AxStateTaskReference(anomalyDetectionDecideState.getKey(), anomalyDetectionDecideTask2.getKey(),
+                        AxStateTaskOutputType.DIRECT, adDec2Act.getKey()));
+
+        logicReader.setDefaultLogic("DefaultState_Logic");
+
+        final AxState anomalyDetectionEstablishState =
+                new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Establish"));
+        anomalyDetectionEstablishState.setTrigger(anomalyDetectionMatchEvent.getKey());
+        final AxStateOutput adEst2Dec = new AxStateOutput(anomalyDetectionEstablishState.getKey(),
+                anomalyDetectionDecideState.getKey(), anomalyDetectionEstablishEvent.getKey());
+        anomalyDetectionEstablishState.getStateOutputs().put(adEst2Dec.getKey().getLocalName(), adEst2Dec);
+        anomalyDetectionEstablishState.setTaskSelectionLogic(new AxTaskSelectionLogic(
+                anomalyDetectionEstablishState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+        anomalyDetectionEstablishState.setDefaultTask(anomalyDetectionEstablishTask.getKey());
+        anomalyDetectionEstablishState.getTaskReferences().put(anomalyDetectionEstablishTask.getKey(),
+                new AxStateTaskReference(anomalyDetectionEstablishState.getKey(),
+                        anomalyDetectionEstablishTask.getKey(), AxStateTaskOutputType.DIRECT, adEst2Dec.getKey()));
+
+        final AxState anomalyDetectionMatchState =
+                new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Match"));
+        anomalyDetectionMatchState.setTrigger(anomalyDetectionTriggerEvent.getKey());
+        final AxStateOutput adMat2Est = new AxStateOutput(anomalyDetectionMatchState.getKey(),
+                anomalyDetectionEstablishState.getKey(), anomalyDetectionMatchEvent.getKey());
+        anomalyDetectionMatchState.getStateOutputs().put(adMat2Est.getKey().getLocalName(), adMat2Est);
+        anomalyDetectionMatchState.setTaskSelectionLogic(new AxTaskSelectionLogic(anomalyDetectionMatchState.getKey(),
+                "TaskSelectionLogic", "MVEL", logicReader));
+        anomalyDetectionMatchState.setDefaultTask(anomalyDetectionMatchTask.getKey());
+        anomalyDetectionMatchState.getTaskReferences().put(anomalyDetectionMatchTask.getKey(),
+                new AxStateTaskReference(anomalyDetectionMatchState.getKey(), anomalyDetectionMatchTask.getKey(),
+                        AxStateTaskOutputType.DIRECT, adMat2Est.getKey()));
+
+        anomalyDetectionPolicy.setFirstState(anomalyDetectionMatchState.getKey().getLocalName());
+        anomalyDetectionPolicy.getStateMap().put(anomalyDetectionMatchState.getKey().getLocalName(),
+                anomalyDetectionMatchState);
+        anomalyDetectionPolicy.getStateMap().put(anomalyDetectionEstablishState.getKey().getLocalName(),
+                anomalyDetectionEstablishState);
+        anomalyDetectionPolicy.getStateMap().put(anomalyDetectionDecideState.getKey().getLocalName(),
+                anomalyDetectionDecideState);
+        anomalyDetectionPolicy.getStateMap().put(anomalyDetectionActState.getKey().getLocalName(),
+                anomalyDetectionActState);
+
+        final AxPolicies anomalyDetectionPolicies =
+                new AxPolicies(new AxArtifactKey("AnomalyDetectionPolicies", "0.0.1"));
+        anomalyDetectionPolicies.getPolicyMap().put(anomalyDetectionPolicy.getKey(), anomalyDetectionPolicy);
+
+        final AxKeyInformation keyInformation =
+                new AxKeyInformation(new AxArtifactKey("AnomalyDetectionKeyInformation", "0.0.1"));
+        final AxPolicyModel anomalyDetectionPolicyModel =
+                new AxPolicyModel(new AxArtifactKey("AnomalyDetectionPolicyModel", "0.0.1"));
+        anomalyDetectionPolicyModel.setPolicies(anomalyDetectionPolicies);
+        anomalyDetectionPolicyModel.setEvents(anomalyDetectionEvents);
+        anomalyDetectionPolicyModel.setTasks(anomalyDetectionTasks);
+        anomalyDetectionPolicyModel.setAlbums(anomalyDetectionAlbums);
+        anomalyDetectionPolicyModel.setSchemas(adContextSchemas);
+        anomalyDetectionPolicyModel.setKeyInformation(keyInformation);
+        anomalyDetectionPolicyModel.getKeyInformation().generateKeyInfo(anomalyDetectionPolicyModel);
+
+        final AxValidationResult result = anomalyDetectionPolicyModel.validate(new AxValidationResult());
+        if (!result.getValidationResult().equals(AxValidationResult.ValidationResult.VALID)) {
+            throw new ApexRuntimeException("model " + anomalyDetectionPolicyModel.getID() + " is not valid" + result);
+        }
+        return anomalyDetectionPolicyModel;
+    }
+
+    /**
+     * Gets the auto learn policy model.
+     *
+     * @return the auto learn policy model
+     */
+    // CHECKSTYLE:OFF: checkstyle:maximumMethodLength
+    public AxPolicyModel getAutoLearnPolicyModel() {
+        // CHECKSTYLE:ON: checkstyle:maximumMethodLength
+        // Data types for event parameters
+        final AxContextSchema monitoredValue =
+                new AxContextSchema(new AxArtifactKey("MonitoredValue", "0.0.1"), "Java", "java.lang.Double");
+
+        final AxContextSchemas alContextSchemas = new AxContextSchemas(new AxArtifactKey("ALDatatypes", "0.0.1"));
+        alContextSchemas.getSchemasMap().put(monitoredValue.getKey(), monitoredValue);
+
+        final AxEvent autoLearnTriggerEvent = new AxEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"),
+                "org.onap.policy.apex.examples.adaptive.events");
+        autoLearnTriggerEvent.setSource("External");
+        autoLearnTriggerEvent.setTarget("Match");
+        autoLearnTriggerEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnTriggerEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        autoLearnTriggerEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnTriggerEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+        final AxEvent autoLearnMatchEvent = new AxEvent(new AxArtifactKey("AutoLearnMatchEvent", "0.0.1"),
+                "org.onap.policy.apex.examples.adaptive.events");
+        autoLearnMatchEvent.setSource("Match");
+        autoLearnMatchEvent.setTarget("Establish");
+        autoLearnMatchEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnMatchEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        autoLearnMatchEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnMatchEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+        final AxEvent autoLearnEstablishEvent = new AxEvent(new AxArtifactKey("AutoLearnEstablishEvent", "0.0.1"),
+                "org.onap.policy.apex.examples.adaptive.events");
+        autoLearnEstablishEvent.setSource("Establish");
+        autoLearnEstablishEvent.setTarget("Decide");
+        autoLearnEstablishEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnEstablishEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        autoLearnEstablishEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnEstablishEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+        final AxEvent autoLearnDecideEvent = new AxEvent(new AxArtifactKey("AutoLearnDecideEvent", "0.0.1"),
+                "org.onap.policy.apex.examples.adaptive.events");
+        autoLearnDecideEvent.setSource("Decide");
+        autoLearnDecideEvent.setTarget("Act");
+        autoLearnDecideEvent.getParameterMap().put("MonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnDecideEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        autoLearnDecideEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnDecideEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+        final AxEvent autoLearnActEvent = new AxEvent(new AxArtifactKey("AutoLearnActEvent", "0.0.1"),
+                "org.onap.policy.apex.examples.adaptive.events");
+        autoLearnActEvent.setSource("Act");
+        autoLearnActEvent.setTarget("External");
+        autoLearnActEvent.getParameterMap().put("MonitoredValue",
+                new AxField(new AxReferenceKey(autoLearnActEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+        autoLearnActEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+                new AxReferenceKey(autoLearnActEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+        final AxEvents autoLearnEvents = new AxEvents(new AxArtifactKey("AutoLearnEvents", "0.0.1"));
+        autoLearnEvents.getEventMap().put(autoLearnTriggerEvent.getKey(), autoLearnTriggerEvent);
+        autoLearnEvents.getEventMap().put(autoLearnMatchEvent.getKey(), autoLearnMatchEvent);
+        autoLearnEvents.getEventMap().put(autoLearnEstablishEvent.getKey(), autoLearnEstablishEvent);
+        autoLearnEvents.getEventMap().put(autoLearnDecideEvent.getKey(), autoLearnDecideEvent);
+        autoLearnEvents.getEventMap().put(autoLearnActEvent.getKey(), autoLearnActEvent);
+
+        // Data types for context
+        final AxContextSchema autoLearn = new AxContextSchema(new AxArtifactKey("AutoLearn", "0.0.1"), "Java",
+                "org.onap.policy.apex.examples.adaptive.concepts.AutoLearn");
+        alContextSchemas.getSchemasMap().put(autoLearn.getKey(), autoLearn);
+
+        // One context map
+        final AxContextAlbum autoLearnAlbum = new AxContextAlbum(new AxArtifactKey("AutoLearnAlbum", "0.0.1"),
+                "APPLICATION", true, autoLearn.getKey());
+
+        final AxContextAlbums autoLearnAlbums = new AxContextAlbums(new AxArtifactKey("AutoLearnContext", "0.0.1"));
+        autoLearnAlbums.getAlbumsMap().put(autoLearnAlbum.getKey(), autoLearnAlbum);
+
+        // Tasks
+        final AxLogicReader logicReader = new PolicyLogicReader()
+                .setLogicPackage(this.getClass().getPackage().getName()).setDefaultLogic("DefaultAutoLearnTask_Logic");
+
+        final AxTask autoLearnMatchTask = new AxTask(new AxArtifactKey("AutoLearnMatchTask", "0.0.1"));
+        autoLearnMatchTask.duplicateInputFields(autoLearnTriggerEvent.getParameterMap());
+        autoLearnMatchTask.duplicateOutputFields(autoLearnMatchEvent.getParameterMap());
+        autoLearnMatchTask.setTaskLogic(new AxTaskLogic(autoLearnMatchTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask autoLearnEstablishTask = new AxTask(new AxArtifactKey("AutoLearnEstablishTask", "0.0.1"));
+        autoLearnEstablishTask.duplicateInputFields(autoLearnMatchEvent.getParameterMap());
+        autoLearnEstablishTask.duplicateOutputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnEstablishTask
+                .setTaskLogic(new AxTaskLogic(autoLearnEstablishTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        logicReader.setDefaultLogic(null);
+
+        final AxTask autoLearnDecideTask0 = new AxTask(new AxArtifactKey("AutoLearnDecideTask0", "0.0.1"));
+        autoLearnDecideTask0.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnDecideTask0.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnDecideTask0
+                .setTaskLogic(new AxTaskLogic(autoLearnDecideTask0.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask autoLearnDecideTask1 = new AxTask(new AxArtifactKey("AutoLearnDecideTask1", "0.0.1"));
+        autoLearnDecideTask1.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnDecideTask1.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnDecideTask1
+                .setTaskLogic(new AxTaskLogic(autoLearnDecideTask1.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask autoLearnDecideTask2 = new AxTask(new AxArtifactKey("AutoLearnDecideTask2", "0.0.1"));
+        autoLearnDecideTask2.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnDecideTask2.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnDecideTask2
+                .setTaskLogic(new AxTaskLogic(autoLearnDecideTask2.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask autoLearnDecideTask3 = new AxTask(new AxArtifactKey("AutoLearnDecideTask3", "0.0.1"));
+        autoLearnDecideTask3.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnDecideTask3.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnDecideTask3
+                .setTaskLogic(new AxTaskLogic(autoLearnDecideTask3.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask autoLearnDecideTask4 = new AxTask(new AxArtifactKey("AutoLearnDecideTask4", "0.0.1"));
+        autoLearnDecideTask4.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnDecideTask4.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnDecideTask4
+                .setTaskLogic(new AxTaskLogic(autoLearnDecideTask4.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask autoLearnDecideTask5 = new AxTask(new AxArtifactKey("AutoLearnDecideTask5", "0.0.1"));
+        autoLearnDecideTask5.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnDecideTask5.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnDecideTask5
+                .setTaskLogic(new AxTaskLogic(autoLearnDecideTask5.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTask autoLearnDecideTask6 = new AxTask(new AxArtifactKey("AutoLearnDecideTask6", "0.0.1"));
+        autoLearnDecideTask6.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+        autoLearnDecideTask6.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnDecideTask6
+                .setTaskLogic(new AxTaskLogic(autoLearnDecideTask6.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        logicReader.setDefaultLogic("DefaultAutoLearnTask_Logic");
+
+        final AxTask autoLearnActTask = new AxTask(new AxArtifactKey("AutoLearnActTask", "0.0.1"));
+        autoLearnActTask.duplicateInputFields(autoLearnDecideEvent.getParameterMap());
+        autoLearnActTask.duplicateOutputFields(autoLearnActEvent.getParameterMap());
+        autoLearnActTask.setTaskLogic(new AxTaskLogic(autoLearnActTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+        final AxTasks autoLearnTasks = new AxTasks(new AxArtifactKey("AutoLearnTasks", "0.0.1"));
+        autoLearnTasks.getTaskMap().put(autoLearnMatchTask.getKey(), autoLearnMatchTask);
+        autoLearnTasks.getTaskMap().put(autoLearnEstablishTask.getKey(), autoLearnEstablishTask);
+        autoLearnTasks.getTaskMap().put(autoLearnDecideTask0.getKey(), autoLearnDecideTask0);
+        autoLearnTasks.getTaskMap().put(autoLearnDecideTask1.getKey(), autoLearnDecideTask1);
+        autoLearnTasks.getTaskMap().put(autoLearnDecideTask2.getKey(), autoLearnDecideTask2);
+        autoLearnTasks.getTaskMap().put(autoLearnDecideTask3.getKey(), autoLearnDecideTask3);
+        autoLearnTasks.getTaskMap().put(autoLearnDecideTask4.getKey(), autoLearnDecideTask4);
+        autoLearnTasks.getTaskMap().put(autoLearnDecideTask5.getKey(), autoLearnDecideTask5);
+        autoLearnTasks.getTaskMap().put(autoLearnDecideTask6.getKey(), autoLearnDecideTask6);
+        autoLearnTasks.getTaskMap().put(autoLearnActTask.getKey(), autoLearnActTask);
+
+        // Policies
+        logicReader.setDefaultLogic("DefaultState_Logic");
+
+        final AxPolicy autoLearnPolicy = new AxPolicy(new AxArtifactKey("AutoLearnPolicy", "0.0.1"));
+        autoLearnPolicy.setTemplate("MEDA");
+
+        final AxState autoLearnActState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Act"));
+        autoLearnActState.setTrigger(autoLearnDecideEvent.getKey());
+        final AxStateOutput alAct2Out =
+                new AxStateOutput(autoLearnActState.getKey(), AxReferenceKey.getNullKey(), autoLearnActEvent.getKey());
+        autoLearnActState.getStateOutputs().put(alAct2Out.getKey().getLocalName(), alAct2Out);
+        autoLearnActState.setTaskSelectionLogic(
+                new AxTaskSelectionLogic(autoLearnActState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+        autoLearnActState.setDefaultTask(autoLearnActTask.getKey());
+        autoLearnActState.getTaskReferences().put(autoLearnActTask.getKey(),
+                new AxStateTaskReference(autoLearnActState.getKey(), autoLearnActTask.getKey(),
+                        AxStateTaskOutputType.DIRECT, alAct2Out.getKey()));
+
+        logicReader.setDefaultLogic(null);
+
+        final AxState autoLearnDecideState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Decide"));
+        autoLearnDecideState.setTrigger(autoLearnEstablishEvent.getKey());
+        final AxStateOutput alDec2Act = new AxStateOutput(autoLearnDecideState.getKey(), autoLearnActState.getKey(),
+                autoLearnDecideEvent.getKey());
+        autoLearnDecideState.getStateOutputs().put(alDec2Act.getKey().getLocalName(), alDec2Act);
+        autoLearnDecideState.getContextAlbumReferences().add(autoLearnAlbum.getKey());
+        autoLearnDecideState.setTaskSelectionLogic(
+                new AxTaskSelectionLogic(autoLearnDecideState.getKey(), "TaskSelectionLogic", "JAVA", logicReader));
+        autoLearnDecideState.setDefaultTask(autoLearnDecideTask0.getKey());
+        autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask0.getKey(),
+                new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask0.getKey(),
+                        AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+        autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask1.getKey(),
+                new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask1.getKey(),
+                        AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+        autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask2.getKey(),
+                new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask2.getKey(),
+                        AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+        autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask3.getKey(),
+                new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask3.getKey(),
+                        AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+        autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask4.getKey(),
+                new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask4.getKey(),
+                        AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+        autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask5.getKey(),
+                new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask5.getKey(),
+                        AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+        autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask6.getKey(),
+                new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask6.getKey(),
+                        AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+
+        logicReader.setDefaultLogic("DefaultState_Logic");
+
+        final AxState autoLearnEstablishState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Establish"));
+        autoLearnEstablishState.setTrigger(autoLearnMatchEvent.getKey());
+        final AxStateOutput alEst2Dec = new AxStateOutput(autoLearnEstablishState.getKey(),
+                autoLearnDecideState.getKey(), autoLearnEstablishEvent.getKey());
+        autoLearnEstablishState.getStateOutputs().put(alEst2Dec.getKey().getLocalName(), alEst2Dec);
+        autoLearnEstablishState.setTaskSelectionLogic(
+                new AxTaskSelectionLogic(autoLearnEstablishState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+        autoLearnEstablishState.setDefaultTask(autoLearnEstablishTask.getKey());
+        autoLearnEstablishState.getTaskReferences().put(autoLearnEstablishTask.getKey(),
+                new AxStateTaskReference(autoLearnEstablishState.getKey(), autoLearnEstablishTask.getKey(),
+                        AxStateTaskOutputType.DIRECT, alEst2Dec.getKey()));
+
+        final AxState autoLearnMatchState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Match"));
+        autoLearnMatchState.setTrigger(autoLearnTriggerEvent.getKey());
+        final AxStateOutput alMat2Est = new AxStateOutput(autoLearnMatchState.getKey(),
+                autoLearnEstablishState.getKey(), autoLearnMatchEvent.getKey());
+        autoLearnMatchState.getStateOutputs().put(alMat2Est.getKey().getLocalName(), alMat2Est);
+        autoLearnMatchState.setTaskSelectionLogic(
+                new AxTaskSelectionLogic(autoLearnMatchState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+        autoLearnMatchState.setDefaultTask(autoLearnMatchTask.getKey());
+        autoLearnMatchState.getTaskReferences().put(autoLearnMatchTask.getKey(),
+                new AxStateTaskReference(autoLearnMatchState.getKey(), autoLearnMatchTask.getKey(),
+                        AxStateTaskOutputType.DIRECT, alMat2Est.getKey()));
+
+        autoLearnPolicy.setFirstState(autoLearnMatchState.getKey().getLocalName());
+        autoLearnPolicy.getStateMap().put(autoLearnMatchState.getKey().getLocalName(), autoLearnMatchState);
+        autoLearnPolicy.getStateMap().put(autoLearnEstablishState.getKey().getLocalName(), autoLearnEstablishState);
+        autoLearnPolicy.getStateMap().put(autoLearnDecideState.getKey().getLocalName(), autoLearnDecideState);
+        autoLearnPolicy.getStateMap().put(autoLearnActState.getKey().getLocalName(), autoLearnActState);
+
+        final AxPolicies autoLearnPolicies = new AxPolicies(new AxArtifactKey("AutoLearnPolicies", "0.0.1"));
+        autoLearnPolicies.getPolicyMap().put(autoLearnPolicy.getKey(), autoLearnPolicy);
+
+        final AxKeyInformation keyInformation =
+                new AxKeyInformation(new AxArtifactKey("AutoLearnKeyInformation", "0.0.1"));
+        final AxPolicyModel autoLearnPolicyModel =
+                new AxPolicyModel(new AxArtifactKey("AutoLearnPolicyModel", "0.0.1"));
+        autoLearnPolicyModel.setPolicies(autoLearnPolicies);
+        autoLearnPolicyModel.setEvents(autoLearnEvents);
+        autoLearnPolicyModel.setTasks(autoLearnTasks);
+        autoLearnPolicyModel.setAlbums(autoLearnAlbums);
+        autoLearnPolicyModel.setSchemas(alContextSchemas);
+        autoLearnPolicyModel.setKeyInformation(keyInformation);
+        autoLearnPolicyModel.getKeyInformation().generateKeyInfo(autoLearnPolicyModel);
+
+        final AxValidationResult result = autoLearnPolicyModel.validate(new AxValidationResult());
+        if (!result.getValidationResult().equals(AxValidationResult.ValidationResult.VALID)) {
+            throw new ApexRuntimeException("model " + autoLearnPolicyModel.getID() + " is not valid" + result);
+        }
+        return autoLearnPolicyModel;
+    }
+
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java
new file mode 100644
index 000000000..0cc9bd3a2
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java
@@ -0,0 +1,64 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model;
+
+import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
+import org.onap.policy.apex.model.basicmodel.handling.ApexModelSaver;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+/**
+ * This class saves sample domain models to disk in XML and JSON format.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public final class AdaptiveDomainModelSaver {
+    /**
+     * Private default constructor to prevent subclassing.
+     */
+    private AdaptiveDomainModelSaver() {}
+
+    /**
+     * Write the AADM model to args[0].
+     *
+     * @param args Not used
+     * @throws ApexException the apex exception
+     */
+    public static void main(final String[] args) throws ApexException {
+        if (args.length != 1) {
+            System.err.println("usage: " + AdaptiveDomainModelSaver.class.getCanonicalName() + " modelDirectory");
+            return;
+        }
+
+        // Save Anomaly Detection model
+        final AxPolicyModel adPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+        final ApexModelSaver<AxPolicyModel> adModelSaver =
+                new ApexModelSaver<>(AxPolicyModel.class, adPolicyModel, args[0]);
+        adModelSaver.apexModelWriteJSON();
+        adModelSaver.apexModelWriteXML();
+
+        // Save Auto Learn model
+        final AxPolicyModel alPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+        final ApexModelSaver<AxPolicyModel> alModelSaver =
+                new ApexModelSaver<>(AxPolicyModel.class, alPolicyModel, args[0]);
+        alModelSaver.apexModelWriteJSON();
+        alModelSaver.apexModelWriteXML();
+    }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java
new file mode 100644
index 000000000..a0b2a8f40
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java
@@ -0,0 +1,414 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model.java;
+
+import java.util.Arrays;
+import java.util.LinkedHashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.commons.math3.distribution.TDistribution;
+import org.apache.commons.math3.util.FastMath;
+import org.onap.policy.apex.core.engine.executor.context.TaskSelectionExecutionContext;
+import org.onap.policy.apex.examples.adaptive.concepts.AnomalyDetection;
+import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
+import org.slf4j.Logger;
+
+/**
+ * The Class AnomalyDetectionPolicy_Decide_TaskSelectionLogic.
+ */
+// CHECKSTYLE:OFF: checkstyle:className
+public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
+    // CHECKSTYLE:ON: checkstyle:className
+
+    private Logger logger;
+    // configuration
+    private static final double ANOMALY_SENSITIVITY = 0.05;
+    private static final int FREQUENCY = 360;
+
+    /**
+     * A map to hold the Anomaly degree/levels/probabilities required for each task.<br>
+     * If there is no task defined for a calculated anomaly-degree, then the default task is used.<br>
+     * The map use (LinkedHashMap) is an insertion-ordered map, so the first interval matching a query is used.
+     */
+    // CHECKSTYLE:OFF: checkstyle:magicNumber
+    private static final Map<double[], String> TASK_INTERVALS = new LinkedHashMap<>();
+    static {
+        TASK_INTERVALS.put(new double[] { 0.0, 0.1 }, null); // null will mean default task
+        TASK_INTERVALS.put(new double[] { 0.25, 0.5 }, "AnomalyDetectionDecideTask1");
+        TASK_INTERVALS.put(new double[] { 0.5, 1.01 }, "AnomalyDetectionDecideTask2");
+    }
+    // CHECKSTYLE:ON: checkstyle:magicNumber
+
+    private volatile TaskSelectionExecutionContext executionContext;
+
+    /**
+     * Gets the task.
+     *
+     * @param executor the executor
+     * @return the task
+     */
+    public boolean getTask(final TaskSelectionExecutionContext executor) {
+        executionContext = executor;
+        logger = executionContext.logger;
+        logger.debug(executor.subject.getId());
+        logger.debug(executor.inFields.toString());
+        final double now = (Double) (executor.inFields.get("MonitoredValue"));
+        final Integer iteration = (Integer) (executor.inFields.get("Iteration"));
+        final double[] vals = this.forecastingAndAnomaly(now); // double[forecastedValue, AnomalyScore,
+                                                               // AnomalyProbability]
+        final double anomalyness = vals[2];
+        String task = null;
+        for (final Map.Entry<double[], String> i : TASK_INTERVALS.entrySet()) {
+            if (checkInterval(anomalyness, i.getKey())) {
+                task = i.getValue();
+                break;
+            }
+        }
+        if (task == null) {
+            executionContext.subject.getDefaultTaskKey().copyTo(executionContext.selectedTask);
+        } else {
+            executionContext.subject.getTaskKey(task).copyTo(executionContext.selectedTask);
+        }
+        if (logger.isDebugEnabled()) {
+            logger.debug(
+                    "TestAnomalyDetectionTSLPolicy0000DecideStateTaskSelectionLogic.getTask():\t************\t\t\t\t"
+                            + "Iteration:\t" + iteration + "\tValue:\t" + now + "\tForecast:\t" + vals[0]
+                            + "\tAnomalyScore:\t" + vals[1] + "\tAnomalyProbability:\t" + vals[2] + "\tInvoking Task:\t"
+                            + executionContext.selectedTask);
+        }
+        return true;
+    }
+
+    /**
+     * Anomaly detection and forecast.
+     *
+     * @param value The current value
+     * @return Null if the function can not be executed correctly, otherwise double[forecastedValue, AnomalyScore,
+     *         AnomalyProbability]
+     */
+    public double[] forecastingAndAnomaly(final double value) {
+        try {
+            executionContext.getContextAlbum("AnomalyDetectionAlbum").lockForWriting("AnomalyDetection");
+        } catch (final ApexException e) {
+            logger.error("Failed to acquire write lock on \"AnomalyDetection\" context", e);
+            return null;
+        }
+
+        // Get the context object
+        AnomalyDetection anomalyDetection =
+                (AnomalyDetection) executionContext.getContextAlbum("AnomalyDetectionAlbum").get("AnomalyDetection");
+        if (anomalyDetection == null) {
+            anomalyDetection = new AnomalyDetection();
+            executionContext.getContextAlbum("AnomalyDetectionAlbum").put("AnomalyDetection", anomalyDetection);
+        }
+
+        // Check the lists are initialized
+        if (!anomalyDetection.isInitialized()) {
+            anomalyDetection.init(FREQUENCY);
+        }
+
+        boolean unsetfirstround = false;
+
+        int frequency = anomalyDetection.getFrequency();
+        frequency = frequency + 1;
+
+        // reset frequency counter
+        if (frequency >= FREQUENCY) {
+            unsetfirstround = true;
+            frequency = 0;
+        }
+        anomalyDetection.setFrequency(frequency);
+
+        if (unsetfirstround && anomalyDetection.getFirstRound()) {
+            anomalyDetection.setFirstRound(false);
+        }
+
+        // --------- calculate the forecasted value - simple version
+        final Double lastForecast = anomalyDetection.getFrequencyForecasted().get(frequency);
+
+        // get forecast for current value
+        final double forecastedValue = lastForecast == null ? value : expMovingAverage(value, lastForecast);
+
+        // --------- calculate the anomalyScore
+        final double anomalyScore = lastForecast == null ? 0.0 : FastMath.abs(lastForecast - value);
+
+        anomalyDetection.getFrequencyForecasted().set(frequency, forecastedValue);
+
+        // anomaly score is ignored in the first frequency period
+        if (!anomalyDetection.getFirstRound()) {
+            ((LinkedList<Double>) anomalyDetection.getAnomalyScores()).addLast(anomalyScore);
+        }
+
+        // CHECKSTYLE:OFF: checkstyle:magicNumber
+        // max FREQUENCY*4 anomaly scores history
+        listSizeControl(anomalyDetection.getAnomalyScores(), FREQUENCY * 4);
+
+        // ---------- calculate the anomaly probability
+        double anomalyProbability = 0.0;
+        if (anomalyDetection.getAnomalyScores().size() > 30) {
+            // 0.5
+            anomalyProbability = gStatsTest(anomalyDetection.getAnomalyScores(), ANOMALY_SENSITIVITY);
+        }
+        // CHECKSTYLE:ON: checkstyle:magicNumber
+
+        try {
+            executionContext.getContextAlbum("AnomalyDetectionAlbum").unlockForWriting("AnomalyDetection");
+        } catch (final ApexException e) {
+            logger.error("Failed to release write lock on \"AnomalyDetection\" context", e);
+            return null;
+        }
+
+        return new double[] { forecastedValue, anomalyScore, anomalyProbability };
+    }
+
+    /**
+     * Is the passed value inside the interval, i.e. (value<interval[1] && value>=interval[0])
+     *
+     * @param value The value to check
+     * @param interval A 2 element double array describing an interval
+     * @return true if the value is between interval[0] (inclusive) and interval[1] (exclusive), i.e. (value<interval[1]
+     *         && value>=interval[0]). Otherwise false;
+     */
+    private static boolean checkInterval(final double value, final double[] interval) {
+        if (interval == null || interval.length != 2) {
+            throw new IllegalArgumentException("something other than an interval passed to checkInterval");
+        }
+        final double min = interval[0];
+        final double max = interval[1];
+        return (value < max && value >= min);
+    }
+
+    /**
+     * calculate the anomaly probability using statistical test.
+     *
+     * @param values the values
+     * @param significanceLevel the significance level
+     * @return the double
+     */
+    private static double gStatsTest(final List<Double> values, final double significanceLevel) {
+        if (isAllEqual(values)) {
+            return 0.0;
+        }
+        // the targeted value or the last value
+        final double currentV = values.get(values.size() - 1);
+        Double[] lValuesCopy = values.toArray(new Double[values.size()]);
+        Arrays.sort(lValuesCopy); // takes ~40% of method time
+        // if(logger.isDebugEnabled()){
+        // logger.debug("values:" + Arrays.toString(lValuesCopy));
+        // }
+        // get mean
+        double mean = getMean(lValuesCopy);
+        // get the test value: v
+        double v = getV(lValuesCopy, mean, true);
+        // get the p value for the test value
+        double pValue = getPValue(lValuesCopy, v, mean); // takes approx 25% of method time
+        // if(logger.isDebugEnabled()){
+        // logger.debug("pValue:" + pValue);
+        // }
+
+        // check the critical level
+        while (pValue < significanceLevel) { // takes approx 20% of method time
+            // the score value as the anomaly probability
+            final double score = (significanceLevel - pValue) / significanceLevel;
+            if (v == currentV) {
+                return score;
+            }
+            // do the critical check again for the left values
+            lValuesCopy = removevalue(lValuesCopy, v);
+            if (isAllEqual(lValuesCopy)) {
+                return 0.0;
+            }
+            // if(logger.isDebugEnabled()){
+            // logger.debug("left values:" + Arrays.toString(lValuesCopy));
+            // }
+            mean = getMean(lValuesCopy);
+            v = getV(lValuesCopy, mean, true);
+            pValue = getPValue(lValuesCopy, v, mean);
+        }
+        return 0.0;
+    }
+
+    /**
+     * get the test value based on mean from sorted values.
+     *
+     * @param lValues the l values
+     * @param mean the mean
+     * @param maxValueOnly : only the max extreme value will be tested
+     * @return the value to be tested
+     */
+    private static double getV(final Double[] lValues, final double mean, final boolean maxValueOnly) {
+        double v = lValues[lValues.length - 1];
+        // max value as the extreme value
+        if (maxValueOnly) {
+            return v;
+        }
+        // check the extreme side
+        if ((v - mean) < (mean - lValues[0])) {
+            v = lValues[0];
+        }
+        return v;
+    }
+
+    /**
+     * calculate the P value for the t distribution.
+     *
+     * @param lValues the l values
+     * @param v the v
+     * @param mean the mean
+     * @return the p value
+     */
+    private static double getPValue(final Double[] lValues, final double v, final double mean) {
+        // calculate z value
+        final double z = FastMath.abs(v - mean) / getStdDev(lValues, mean);
+        // logger.debug("z: " + z);
+        // calculate T
+        final double n = lValues.length;
+        final double s = (z * z * n * (2.0 - n)) / (z * z * n - (n - 1.0) * (n - 1.0));
+        final double t = FastMath.sqrt(s);
+        // logger.debug("t:" + t);
+        // default p value = 0
+        double pValue = 0.0;
+        if (!Double.isNaN(t)) {
+            // t distribution with n-2 degrees of freedom
+            final TDistribution tDist = new TDistribution(n - 2);
+            pValue = n * (1.0 - tDist.cumulativeProbability(t));
+            // set max pValue = 1
+            pValue = pValue > 1.0 ? 1.0 : pValue;
+        }
+        // logger.debug("value: "+ v + " , pValue: " + pValue);
+        return pValue;
+    }
+
+    /*
+     * Some utility methods
+     */
+    // exponential = 2(n+1)
+    private static final double EMA_EXPONENT = 2.0 / (7.0 + 1.0);
+    private static final double EMA_EXPONENT_1 = (1.0 - EMA_EXPONENT);
+
+    /**
+     * exponential moving average.
+     *
+     * @param value the value
+     * @param lastForecast the last forecast
+     * @return the double
+     */
+    private static double expMovingAverage(final double value, final double lastForecast) {
+        return (value * EMA_EXPONENT) + (lastForecast * EMA_EXPONENT_1);
+    }
+
+    /**
+     * Remove the first occurence of the value v from the array.
+     *
+     * @param lValues the l values
+     * @param v the v
+     * @return the double[]
+     */
+    private static Double[] removevalue(final Double[] lValues, final double v) {
+        for (int i = 0; i < lValues.length; i++) {
+            if (lValues[i] == v) {
+                final Double[] ret = new Double[lValues.length - 1];
+                System.arraycopy(lValues, 0, ret, 0, i);
+                System.arraycopy(lValues, i + 1, ret, i, lValues.length - i - 1);
+                return ret;
+            }
+        }
+        return lValues;
+    }
+
+    /**
+     * get mean value of double list.
+     *
+     * @param lValues the l values
+     * @return the mean
+     */
+    private static double getMean(final Double[] lValues) {
+        double sum = 0.0;
+        for (final double d : lValues) {
+
+            sum += d;
+        }
+        return sum / lValues.length;
+    }
+
+    /**
+     * get standard deviation of double list.
+     *
+     * @param lValues the l values
+     * @param mean the mean
+     * @return stddev
+     */
+    private static double getStdDev(final Double[] lValues, final double mean) {
+        double temp = 0.0;
+        for (final double d : lValues) {
+            temp += (mean - d) * (mean - d);
+        }
+        return FastMath.sqrt(temp / lValues.length);
+    }
+
+    /**
+     * Chop head off list to make it length max .
+     *
+     * @param list the list to chop
+     * @param max the max size
+     */
+    private static void listSizeControl(final List<?> list, final int max) {
+        final int k = list.size();
+        if (k > max) {
+            // Chop the head off the list.
+            list.subList(0, k - max).clear();
+        }
+    }
+
+    /**
+     * return true if all values are equal.
+     *
+     * @param lValues the l values
+     * @return true, if checks if is all equal
+     */
+    private static boolean isAllEqual(final List<Double> lValues) {
+        final double first = lValues.get(0);
+        for (final Double d : lValues) {
+            if (d != first) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+    /**
+     * return true if all values are equal.
+     *
+     * @param lValues the l values
+     * @return true, if checks if is all equal
+     */
+    private static boolean isAllEqual(final Double[] lValues) {
+        final double first = lValues[0];
+        for (final Double d : lValues) {
+            if (d != first) {
+                return false;
+            }
+        }
+        return true;
+    }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicy_Decide_TaskSelectionLogic.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicy_Decide_TaskSelectionLogic.java
new file mode 100644
index 000000000..23d4e2486
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicy_Decide_TaskSelectionLogic.java
@@ -0,0 +1,151 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model.java;
+
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+
+import org.onap.policy.apex.context.ContextException;
+import org.onap.policy.apex.core.engine.executor.context.TaskSelectionExecutionContext;
+import org.onap.policy.apex.examples.adaptive.concepts.AutoLearn;
+
+/**
+ * The Class AutoLearnPolicy_Decide_TaskSelectionLogic.
+ */
+// CHECKSTYLE:OFF: checkstyle:typeName
+public class AutoLearnPolicy_Decide_TaskSelectionLogic {
+    // CHECKSTYLE:ON: checkstyle:typeName
+    private static final Random RAND = new Random(System.currentTimeMillis());
+    private static final double WANT = 50.0;
+    private int size;
+
+    /**
+     * Gets the task.
+     *
+     * @param executor the executor
+     * @return the task
+     */
+    public boolean getTask(final TaskSelectionExecutionContext executor) {
+        executor.logger.debug(executor.subject.getId());
+        executor.logger.debug(executor.inFields.toString());
+        final List<String> tasks = executor.subject.getTaskNames();
+        size = tasks.size();
+
+        try {
+            executor.getContextAlbum("AutoLearnAlbum").lockForWriting("AutoLearn");
+        } catch (final ContextException e) {
+            executor.logger.error("Failed to acquire write lock on \"autoLearn\" context", e);
+            return false;
+        }
+
+        // Get the context object
+        AutoLearn autoLearn = (AutoLearn) executor.getContextAlbum("AutoLearnAlbum").get("AutoLearn");
+        if (autoLearn == null) {
+            autoLearn = new AutoLearn();
+        }
+
+        // Check the lists are initialized
+        if (!autoLearn.isInitialized()) {
+            autoLearn.init(size);
+        }
+
+        final double now = (Double) (executor.inFields.get("MonitoredValue"));
+        final double diff = now - WANT;
+        final int option = getOption(diff, autoLearn);
+        learn(option, diff, autoLearn);
+
+        executor.getContextAlbum("AutoLearnAlbum").put("AutoLearnAlbum", autoLearn);
+
+        try {
+            executor.getContextAlbum("AutoLearnAlbum").unlockForWriting("AutoLearn");
+        } catch (final ContextException e) {
+            executor.logger.error("Failed to acquire write lock on \"autoLearn\" context", e);
+            return false;
+        }
+
+        executor.subject.getTaskKey(tasks.get(option)).copyTo(executor.selectedTask);
+        return true;
+    }
+
+    /**
+     * Gets the option.
+     *
+     * @param diff the diff
+     * @param autoLearn the auto learn
+     * @return the option
+     */
+    private int getOption(final double diff, final AutoLearn autoLearn) {
+        final Double[] avdiffs = autoLearn.getAvDiffs().toArray(new Double[autoLearn.getAvDiffs().size()]);
+        final int r = RAND.nextInt(size);
+        int closestupi = -1;
+        int closestdowni = -1;
+        double closestup = Double.MAX_VALUE;
+        double closestdown = Double.MIN_VALUE;
+        for (int i = 0; i < size; i++) {
+            if (Double.isNaN(avdiffs[i])) {
+                return r;
+            }
+            if (avdiffs[i] >= diff && avdiffs[i] <= closestup) {
+                closestup = avdiffs[i];
+                closestupi = i;
+            }
+            if (avdiffs[i] <= diff && avdiffs[i] >= closestdown) {
+                closestdown = avdiffs[i];
+                closestdowni = i;
+            }
+        }
+        if (closestupi == -1 || closestdowni == -1) {
+            return r;
+        }
+        if (closestupi == closestdowni) {
+            return closestupi;
+        }
+        if (Math.abs(closestdown - diff) > Math.abs(closestup - diff)) {
+            return closestupi;
+        } else {
+            return closestdowni;
+        }
+    }
+
+    /**
+     * Learn.
+     *
+     * @param option the option
+     * @param diff the diff
+     * @param autoLearn the auto learn
+     */
+    private void learn(final int option, final double diff, final AutoLearn autoLearn) {
+        final Double[] avdiffs = autoLearn.getAvDiffs().toArray(new Double[autoLearn.getAvDiffs().size()]);
+        final Long[] counts = autoLearn.getCounts().toArray(new Long[autoLearn.getCounts().size()]);
+        if (option < 0 || option >= avdiffs.length) {
+            throw new IllegalArgumentException("Error: option" + option);
+        }
+        counts[option]++;
+        if (Double.isNaN(avdiffs[option])) {
+            avdiffs[option] = diff;
+        } else {
+            avdiffs[option] = (avdiffs[option] * (counts[option] - 1) + diff) / counts[option];
+        }
+        autoLearn.setAvDiffs(Arrays.asList(avdiffs));
+        autoLearn.setCounts(Arrays.asList(counts));
+    }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/package-info.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/package-info.java
new file mode 100644
index 000000000..172be5ada
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Pprovides task selection logic for the adaptive domain in Java.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive.model.java;
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/package-info.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/package-info.java
new file mode 100644
index 000000000..d38333ba8
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Provides Java generation of the Adaptive model as well as Java task selection logic for the domain.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive.model;
diff --git a/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AnomalyDetectionEvents.json b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AnomalyDetectionEvents.json
new file mode 100644
index 000000000..2872f5ae4
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AnomalyDetectionEvents.json
@@ -0,0 +1,200 @@
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 0,
+    "MonitoredValue": 0.04818448765182781
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.016856740089812434,
+    "Iteration": 1
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 2,
+    "MonitoredValue": 0.07283031742656552
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 3,
+    "MonitoredValue": 0.05284674407712258
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.08685406312054446,
+    "Iteration": 4
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.09580382706345814,
+    "Iteration": 5
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 6,
+    "MonitoredValue": 0.07022220893167114
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.05027456879617627,
+    "Iteration": 7
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 8,
+    "MonitoredValue": 0.16867139038930964
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.18383434112759453,
+    "Iteration": 9
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 10,
+    "MonitoredValue": 0.11940188164467769
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 11,
+    "MonitoredValue": 0.18528189685974508
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.19934812597926355,
+    "Iteration": 12
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.2769320946213342,
+    "Iteration": 13
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 14,
+    "MonitoredValue": 0.2573073533232842
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 15,
+    "MonitoredValue": 0.2114736039819203
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.3070179558734792,
+    "Iteration": 16
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.22817748171407137,
+    "Iteration": 17
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "Iteration": 18,
+    "MonitoredValue": 0.3742338965886025
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AnomalyDetectionTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 0.33767889848226273,
+    "Iteration": 19
+}
+
diff --git a/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AutoLearnEvents.json b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AutoLearnEvents.json
new file mode 100644
index 000000000..4a90c944d
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AutoLearnEvents.json
@@ -0,0 +1,10000 @@
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 0.0,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 77.69433155254976,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 78.80403683294003
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 78.8005362087235,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 74.25243852072533
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 77.70005535199167
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 74.25243852072533
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
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+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 74.25243852072533
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 74.25243852072533
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 77.43985768687475
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 77.22866213897053
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 74.25243852072533
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 74.25243852072533
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 77.89674604484705,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 75.24961836787988
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 74.25243852072533
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 77.01106311345667
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 90.42073086787556,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 79.64186930310875
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 63.47357695595852
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "LastMonitoredValue": 71.55772312953363,
+    "MonitoredValue": 76.94715391191704
+}
+
+{
+    "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+    "name": "AutoLearnTriggerEvent",
+    "version": "0.0.1",
+    "source": "CompaA",
+    "target": "apex",
+    "MonitoredValue": 76.94715391191704,
+    "LastMonitoredValue": 82.33658469430046
+}
+
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask0_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask0_TaskLogic.mvel
new file mode 100644
index 000000000..758062b8e
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask0_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask0: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (1.0/2.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+    ret = MAX;
+if (ret<MIN)
+    ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask0: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask1_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask1_TaskLogic.mvel
new file mode 100644
index 000000000..051766045
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask1_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask1: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (1.0/5.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+    ret = MAX;
+if (ret<MIN)
+    ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask1: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask2_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask2_TaskLogic.mvel
new file mode 100644
index 000000000..b787b2153
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask2_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask2: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (1.0/10.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+    ret = MAX;
+if (ret<MIN)
+    ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask2: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask3_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask3_TaskLogic.mvel
new file mode 100644
index 000000000..7fbc8c765
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask3_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask3: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = rand.nextGaussian()*((MAX - WANT) / 50.0);
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+    ret = MAX;
+if (ret<MIN)
+    ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask3: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask4_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask4_TaskLogic.mvel
new file mode 100644
index 000000000..a0a12d84f
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask4_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask4: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (-1.0/10.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+    ret = MAX;
+if (ret<MIN)
+    ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask4: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask5_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask5_TaskLogic.mvel
new file mode 100644
index 000000000..fc2005fdf
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask5_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask5: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (-1.0/5.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+    ret = MAX;
+if (ret < MIN)
+    ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask5: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask6_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask6_TaskLogic.mvel
new file mode 100644
index 000000000..5eb44839c
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask6_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask6: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (-1.0/2.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+    ret = MAX;
+if (ret<MIN)
+    ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask6: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAnomalyDetectionTask_Logic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAnomalyDetectionTask_Logic.mvel
new file mode 100644
index 000000000..8f894d297
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAnomalyDetectionTask_Logic.mvel
@@ -0,0 +1,25 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug(subject.getTaskName() + " execution logic");
+logger.debug("inFields : " + inFields);
+outFields["MonitoredValue"] = inFields["MonitoredValue"];
+outFields["Iteration"] = inFields["Iteration"];
+logger.debug("outFields : "+outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAutoLearnTask_Logic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAutoLearnTask_Logic.mvel
new file mode 100644
index 000000000..636378396
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAutoLearnTask_Logic.mvel
@@ -0,0 +1,26 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnActTask execution logic");
+logger.debug("inFields : " + inFields);
+outFields["MonitoredValue"] = inFields["MonitoredValue"];
+outFields["LastMonitoredValue"] = inFields["LastMonitoredValue"];
+logger.debug("outFields : " + outFields);
+return true;
+
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultState_Logic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultState_Logic.mvel
new file mode 100644
index 000000000..2db0a0031
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultState_Logic.mvel
@@ -0,0 +1,23 @@
+/*
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * 
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug(subject.id + ":" + subject.stateName);
+subject.defaultTaskKey.copyTo(selectedTask);
+logger.debug(subject.id + ":" + subject.stateName) + ", using default task" + subject.defaultTaskKey.getID();
+return true;
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java
new file mode 100644
index 000000000..898531c32
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java
@@ -0,0 +1,60 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAnomalyDetectionDBWrite {
+    private Connection connection;
+    TestApexModel<AxPolicyModel> testApexModel;
+
+    @Before
+    public void setup() throws Exception {
+        Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+        connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+        testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAnomalyDetectionModelCreator());
+    }
+
+    @After
+    public void teardown() throws Exception {
+        connection.close();
+        new File("derby.log").delete();
+    }
+
+    @Test
+    public void testModelWriteReadJPA() throws Exception {
+        final DAOParameters daoParameters = new DAOParameters();
+        daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+        daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+        testApexModel.testApexModelWriteReadJPA(daoParameters);
+    }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java
new file mode 100644
index 000000000..7e2fb0eb7
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java
@@ -0,0 +1,81 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertTrue;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAnomalyDetectionModel {
+    private Connection connection;
+    TestApexModel<AxPolicyModel> testApexModel;
+
+    @Before
+    public void setup() throws Exception {
+        Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+        connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+        testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAnomalyDetectionModelCreator());
+    }
+
+    @After
+    public void teardown() throws Exception {
+        connection.close();
+        new File("derby.log").delete();
+    }
+
+    @Test
+    public void testModelValid() throws Exception {
+        final AxValidationResult result = testApexModel.testApexModelValid();
+        assertTrue(result.toString().equals(VALID_MODEL_STRING));
+    }
+
+    @Test
+    public void testModelWriteReadXML() throws Exception {
+        testApexModel.testApexModelWriteReadXML();
+    }
+
+    @Test
+    public void testModelWriteReadJSON() throws Exception {
+        testApexModel.testApexModelWriteReadJSON();
+    }
+
+    @Test
+    public void testModelWriteReadJPA() throws Exception {
+        final DAOParameters daoParameters = new DAOParameters();
+        daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+        daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+        testApexModel.testApexModelWriteReadJPA(daoParameters);
+    }
+
+    private static final String VALID_MODEL_STRING = "***validation of model successful***";
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java
new file mode 100644
index 000000000..2b50d69ab
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java
@@ -0,0 +1,56 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModelCreator;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+/**
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public class TestAnomalyDetectionModelCreator implements TestApexModelCreator<AxPolicyModel> {
+
+    @Override
+    public AxPolicyModel getModel() {
+        return new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+    }
+
+    @Override
+    public AxPolicyModel getMalstructuredModel() {
+        return null;
+    }
+
+    @Override
+    public AxPolicyModel getObservationModel() {
+        return null;
+    }
+
+    @Override
+    public AxPolicyModel getWarningModel() {
+        return getModel();
+    }
+
+    @Override
+    public AxPolicyModel getInvalidModel() {
+        return null;
+    }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java
new file mode 100644
index 000000000..7d9791d46
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java
@@ -0,0 +1,164 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.IOException;
+import java.util.Random;
+
+import org.junit.Test;
+import org.onap.policy.apex.core.engine.EngineParameters;
+import org.onap.policy.apex.core.engine.engine.ApexEngine;
+import org.onap.policy.apex.core.engine.engine.impl.ApexEngineFactory;
+import org.onap.policy.apex.core.engine.event.EnEvent;
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
+import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+import org.onap.policy.apex.plugins.executor.java.JavaExecutorParameters;
+import org.onap.policy.apex.plugins.executor.mvel.MVELExecutorParameters;
+import org.slf4j.ext.XLogger;
+import org.slf4j.ext.XLoggerFactory;
+
+/**
+ * This policy passes, and recieves a Double event context filed called "EVCDouble".<br>
+ * The policy tries to detect anomalies in the pattern of values for EVCDouble<br>
+ * See the 2 test cases below (1 short, 1 long)
+ *
+ * @author John Keeney (John.Keeney@ericsson.com)
+ */
+public class TestAnomalyDetectionTSLUseCase {
+    private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAnomalyDetectionTSLUseCase.class);
+
+    private static final int MAXITERATIONS = 3660;
+    private static final Random RAND = new Random(System.currentTimeMillis());
+
+    @Test
+    // once through the long running test below
+    public void TestAnomalyDetectionTSL() throws ApexException, InterruptedException, IOException {
+        final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+        assertNotNull(apexPolicyModel);
+
+        final AxValidationResult validationResult = new AxValidationResult();
+        apexPolicyModel.validate(validationResult);
+        assertTrue(validationResult.isValid());
+
+        final AxArtifactKey key = new AxArtifactKey("AnomalyTSLApexEngine", "0.0.1");
+        final EngineParameters parameters = new EngineParameters();
+        parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+        parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+        final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+        final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+        apexEngine1.addEventListener("listener", listener1);
+        apexEngine1.updateModel(apexPolicyModel);
+        apexEngine1.start();
+        final EnEvent triggerEvent =
+                apexEngine1.createEvent(new AxArtifactKey("AnomalyDetectionTriggerEvent", "0.0.1"));
+        final double rval = RAND.nextGaussian();
+        triggerEvent.put("Iteration", 0);
+        triggerEvent.put("MonitoredValue", rval);
+        LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
+        apexEngine1.handleEvent(triggerEvent);
+        final EnEvent result = listener1.getResult();
+        LOGGER.info("Receiving action event {} ", result);
+        assertEquals("ExecutionIDs are different", triggerEvent.getExecutionID(), result.getExecutionID());
+        triggerEvent.clear();
+        result.clear();
+        Thread.sleep(1);
+        apexEngine1.stop();
+    }
+
+    /**
+     * This policy passes, and recieves a Double event context filed called "EVCDouble"<br>
+     * The policy tries to detect anomalies in the pattern of values for EVCDouble <br>
+     * This test case generates a SineWave-like pattern for the parameter, repeating every 360 iterations. (These Period
+     * should probably be set using TaskParameters!) Every 361st value is a random number!, so should be identified as
+     * an Anomaly. The policy has 3 Decide Tasks, and the Decide TaskSelectionLogic picks one depending on the
+     * 'Anomaliness' of the input data. <br>
+     * To plot the results grep debug results for the string "************", paste into excel and delete non-relevant
+     * columns<br>
+     *
+     * @throws ApexException the apex exception
+     * @throws InterruptedException the interrupted exception
+     * @throws IOException Signals that an I/O exception has occurred.
+     */
+    // Test is disabled by default. uncomment below, or execute using the main() method
+    // @Test
+    // EG Dos command: apex-core.engine> mvn
+    // -Dtest=org.onap.policy.apex.core.engine.ml.TestAnomalyDetectionTSLUseCase test | findstr /L /C:"Apex [main] DEBUG
+    // c.e.a.e.TaskSelectionExecutionLogging -
+    // TestAnomalyDetectionTSL_Policy0000DecideStateTaskSelectionLogic.getTask():"
+    public void TestAnomalyDetectionTSL_main() throws ApexException, InterruptedException, IOException {
+
+        final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+        assertNotNull(apexPolicyModel);
+
+        final AxValidationResult validationResult = new AxValidationResult();
+        apexPolicyModel.validate(validationResult);
+        assertTrue(validationResult.isValid());
+
+        final AxArtifactKey key = new AxArtifactKey("AnomalyTSLApexEngine", "0.0.1");
+        final EngineParameters parameters = new EngineParameters();
+        parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+        parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+        final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+        final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+        apexEngine1.addEventListener("listener1", listener1);
+        apexEngine1.updateModel(apexPolicyModel);
+        apexEngine1.start();
+
+        final EnEvent triggerEvent =
+                apexEngine1.createEvent(new AxArtifactKey("AnomalyDetectionTriggerEvent", "0.0.1"));
+        assertNotNull(triggerEvent);
+
+        for (int iteration = 0; iteration < MAXITERATIONS; iteration++) {
+            // Trigger the policy in engine 1
+
+            double value = (Math.sin(Math.toRadians(iteration))) + (RAND.nextGaussian() / 25.0);
+            // lets make every 361st number a random value to perhaps flag as an anomaly
+            if (((iteration + 45) % 361) == 0) {
+                value = (RAND.nextGaussian() * 2.0);
+            }
+            triggerEvent.put("Iteration", iteration);
+            triggerEvent.put("MonitoredValue", value);
+            LOGGER.info("Iteration " + iteration + ":\tTriggering policy in Engine 1 with " + triggerEvent);
+            apexEngine1.handleEvent(triggerEvent);
+            final EnEvent result = listener1.getResult();
+            LOGGER.info("Iteration " + iteration + ":\tReceiving action event {} ", result);
+            triggerEvent.clear();
+            result.clear();
+        }
+        apexEngine1.stop();
+        Thread.sleep(1000);
+    }
+
+    public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
+        new TestAnomalyDetectionTSLUseCase().TestAnomalyDetectionTSL_main();
+    }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestApexActionListener.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestApexActionListener.java
new file mode 100644
index 000000000..ec7c197b7
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestApexActionListener.java
@@ -0,0 +1,91 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.onap.policy.apex.core.engine.engine.EnEventListener;
+import org.onap.policy.apex.core.engine.event.EnEvent;
+import org.onap.policy.apex.core.infrastructure.threading.ThreadUtilities;
+
+/**
+ * The listener interface for receiving testApexAction events. The class that is interested in processing a
+ * testApexAction event implements this interface, and the object created with that class is registered with a component
+ * using the component's <code>addTestApexActionListener</code> method. When the testApexAction event occurs, that
+ * object's appropriate method is invoked.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public class TestApexActionListener implements EnEventListener {
+    private List<EnEvent> resultEvents = new ArrayList<>();
+
+    private final String id;
+
+    /**
+     * Instantiates a new test apex action listener.
+     *
+     * @param id the id
+     */
+    public TestApexActionListener(final String id) {
+        this.id = id;
+    }
+
+    /**
+     * Gets the result.
+     *
+     * @return the result
+     */
+    public EnEvent getResult() {
+        while (resultEvents.isEmpty()) {
+            ThreadUtilities.sleep(100);
+        }
+        return resultEvents.remove(0);
+    }
+
+    /*
+     * (non-Javadoc)
+     *
+     * @see
+     * org.onap.policy.apex.core.engine.engine.EnEventListener#onEnEvent(org.onap.policy.apex.core.engine.event.EnEvent)
+     */
+    @Override
+    public void onEnEvent(final EnEvent actionEvent) {
+        try {
+            Thread.sleep(100);
+        } catch (final InterruptedException e) {
+            e.printStackTrace();
+        }
+        if (actionEvent != null) {
+            System.out.println("Action event from engine:" + actionEvent.getName());
+            resultEvents.add(actionEvent);
+        }
+    }
+
+    /**
+     * Gets the id.
+     *
+     * @return the id
+     */
+    public String getId() {
+        return id;
+    }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java
new file mode 100644
index 000000000..64efa6c35
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java
@@ -0,0 +1,60 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAutoLearnDBWrite {
+    private Connection connection;
+    TestApexModel<AxPolicyModel> testApexModel;
+
+    @Before
+    public void setup() throws Exception {
+        Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+        connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+        testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAutoLearnModelCreator());
+    }
+
+    @After
+    public void teardown() throws Exception {
+        connection.close();
+        new File("derby.log").delete();
+    }
+
+    @Test
+    public void testModelWriteReadJPA() throws Exception {
+        final DAOParameters daoParameters = new DAOParameters();
+        daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+        daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+        testApexModel.testApexModelWriteReadJPA(daoParameters);
+    }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java
new file mode 100644
index 000000000..aa7f621c9
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java
@@ -0,0 +1,81 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertTrue;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAutoLearnModel {
+    private Connection connection;
+    TestApexModel<AxPolicyModel> testApexModel;
+
+    @Before
+    public void setup() throws Exception {
+        Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+        connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+        testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAutoLearnModelCreator());
+    }
+
+    @After
+    public void teardown() throws Exception {
+        connection.close();
+        new File("derby.log").delete();
+    }
+
+    @Test
+    public void testModelValid() throws Exception {
+        final AxValidationResult result = testApexModel.testApexModelValid();
+        assertTrue(result.toString().equals(VALID_MODEL_STRING));
+    }
+
+    @Test
+    public void testModelWriteReadXML() throws Exception {
+        testApexModel.testApexModelWriteReadXML();
+    }
+
+    @Test
+    public void testModelWriteReadJSON() throws Exception {
+        testApexModel.testApexModelWriteReadJSON();
+    }
+
+    @Test
+    public void testModelWriteReadJPA() throws Exception {
+        final DAOParameters daoParameters = new DAOParameters();
+        daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+        daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+        testApexModel.testApexModelWriteReadJPA(daoParameters);
+    }
+
+    private static final String VALID_MODEL_STRING = "***validation of model successful***";
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java
new file mode 100644
index 000000000..11f1991bf
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java
@@ -0,0 +1,56 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModelCreator;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+/**
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public class TestAutoLearnModelCreator implements TestApexModelCreator<AxPolicyModel> {
+
+    @Override
+    public AxPolicyModel getModel() {
+        return new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+    }
+
+    @Override
+    public AxPolicyModel getMalstructuredModel() {
+        return null;
+    }
+
+    @Override
+    public AxPolicyModel getObservationModel() {
+        return null;
+    }
+
+    @Override
+    public AxPolicyModel getWarningModel() {
+        return getModel();
+    }
+
+    @Override
+    public AxPolicyModel getInvalidModel() {
+        return null;
+    }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java
new file mode 100644
index 000000000..88b504cef
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java
@@ -0,0 +1,187 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.IOException;
+import java.util.Random;
+
+import org.junit.Test;
+import org.onap.policy.apex.core.engine.EngineParameters;
+import org.onap.policy.apex.core.engine.engine.ApexEngine;
+import org.onap.policy.apex.core.engine.engine.impl.ApexEngineFactory;
+import org.onap.policy.apex.core.engine.event.EnEvent;
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
+import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+import org.onap.policy.apex.plugins.executor.java.JavaExecutorParameters;
+import org.onap.policy.apex.plugins.executor.mvel.MVELExecutorParameters;
+import org.slf4j.ext.XLogger;
+import org.slf4j.ext.XLoggerFactory;
+
+/**
+ * Test Auto learning in TSL.
+ *
+ * @author John Keeney (John.Keeney@ericsson.com)
+ */
+public class TestAutoLearnTSLUseCase {
+    private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAutoLearnTSLUseCase.class);
+
+    private static final int MAXITERATIONS = 1000;
+    private static final Random rand = new Random(System.currentTimeMillis());
+
+    @Test
+    // once through the long running test below
+    public void TestAutoLearnTSL() throws ApexException, InterruptedException, IOException {
+        final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+        assertNotNull(apexPolicyModel);
+
+        final AxValidationResult validationResult = new AxValidationResult();
+        apexPolicyModel.validate(validationResult);
+        assertTrue(validationResult.isValid());
+
+        final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
+        final EngineParameters parameters = new EngineParameters();
+        parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+        parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+        final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+        final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+        apexEngine1.addEventListener("listener", listener1);
+        apexEngine1.updateModel(apexPolicyModel);
+        apexEngine1.start();
+        final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
+        final double rval = rand.nextGaussian();
+        triggerEvent.put("MonitoredValue", rval);
+        triggerEvent.put("LastMonitoredValue", 0D);
+        LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
+        apexEngine1.handleEvent(triggerEvent);
+        final EnEvent result = listener1.getResult();
+        LOGGER.info("Receiving action event {} ", result);
+        assertEquals("ExecutionIDs are different", triggerEvent.getExecutionID(), result.getExecutionID());
+        triggerEvent.clear();
+        result.clear();
+        Thread.sleep(1);
+        apexEngine1.stop();
+    }
+
+    /**
+     * This policy passes, and receives a Double event context filed called "EVCDouble"<br>
+     * The policy tries to keep the value at 50, with a Min -100, Max 100 (These should probably be set using
+     * TaskParameters!)<br>
+     * The policy has 7 Decide Tasks that manipulate the value of this field in unknown ways.<br>
+     * The Decide TSL learns the effect of each task, and then selects the appropriate task to get the value back to
+     * 50<br>
+     * After the value settles close to 50 for a while, the test Rests the value to to random number and then
+     * continues<br>
+     * To plot the results grep stdout debug results for the string "*******", paste into excel and delete non-relevant
+     * columns<br>
+     *
+     * @throws ApexException the apex exception
+     * @throws InterruptedException the interrupted exception
+     * @throws IOException Signals that an I/O exception has occurred.
+     */
+    // @Test
+    public void TestAutoLearnTSL_main() throws ApexException, InterruptedException, IOException {
+
+        final double WANT = 50.0;
+        final double toleranceTileJump = 3.0;
+
+        final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+        assertNotNull(apexPolicyModel);
+
+        final AxValidationResult validationResult = new AxValidationResult();
+        apexPolicyModel.validate(validationResult);
+        assertTrue(validationResult.isValid());
+
+        final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
+        final EngineParameters parameters = new EngineParameters();
+        parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+        parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+        final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+        final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+        apexEngine1.addEventListener("listener1", listener1);
+        apexEngine1.updateModel(apexPolicyModel);
+        apexEngine1.start();
+
+        final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
+        assertNotNull(triggerEvent);
+        final double MIN = -100;
+        final double MAX = 100;
+
+        double rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
+        triggerEvent.put("MonitoredValue", rval);
+        triggerEvent.put("LastMonitoredValue", 0);
+
+        double avval = 0;
+        double distance;
+        double avcount = 0;
+
+        for (int iteration = 0; iteration < MAXITERATIONS; iteration++) {
+            // Trigger the policy in engine 1
+            LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
+            apexEngine1.handleEvent(triggerEvent);
+            final EnEvent result = listener1.getResult();
+            LOGGER.info("Receiving action event {} ", result);
+            triggerEvent.clear();
+
+            double val = (Double) result.get("MonitoredValue");
+            final double prevval = (Double) result.get("LastMonitoredValue");
+
+            triggerEvent.put("MonitoredValue", prevval);
+            triggerEvent.put("LastMonitoredValue", val);
+
+            avcount = Math.min((avcount + 1), 20); // maintain average of only the last 20 values
+            avval = ((avval * (avcount - 1)) + val) / (avcount);
+
+            distance = Math.abs(WANT - avval);
+            if (distance < toleranceTileJump) {
+                rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
+                val = rval;
+                triggerEvent.put("MonitoredValue", val);
+                LOGGER.info("Iteration " + iteration + ": Average " + avval + " has become closer (" + distance
+                        + ") than " + toleranceTileJump + " to " + WANT + " so reseting val:\t\t\t\t\t\t\t\t" + val);
+                avval = 0;
+                avcount = 0;
+            }
+            LOGGER.info("Iteration " + iteration + ": \tpreval\t" + prevval + "\tval\t" + val + "\tavval\t" + avval);
+
+            result.clear();
+            Thread.sleep(1);
+        }
+
+        apexEngine1.stop();
+        Thread.sleep(1000);
+
+    }
+
+    public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
+        new TestAutoLearnTSLUseCase().TestAutoLearnTSL_main();
+    }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/package-info.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/package-info.java
new file mode 100644
index 000000000..0e004fe8d
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ * ================================================================================
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Defines domain and policies demonstrating adaptive policies.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive;
diff --git a/examples/examples-adaptive/src/test/resources/META-INF/persistence.xml b/examples/examples-adaptive/src/test/resources/META-INF/persistence.xml
new file mode 100644
index 000000000..7282bb562
--- /dev/null
+++ b/examples/examples-adaptive/src/test/resources/META-INF/persistence.xml
@@ -0,0 +1,69 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ============LICENSE_START=======================================================
+   Copyright (C) 2016-2018 Ericsson. All rights reserved.
+  ================================================================================
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+  
+       http://www.apache.org/licenses/LICENSE-2.0
+  
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  
+  SPDX-License-Identifier: Apache-2.0
+  ============LICENSE_END=========================================================
+-->
+
+<persistence xmlns="http://java.sun.com/xml/ns/persistence" version="2.0">
+    <persistence-unit name="AdaptiveModelsTest" transaction-type="RESOURCE_LOCAL">
+        <provider>org.eclipse.persistence.jpa.PersistenceProvider</provider>
+
+        <class>org.onap.policy.apex.model.basicmodel.dao.converters.CDATAConditioner</class>
+        <class>org.onap.policy.apex.model.basicmodel.dao.converters.UUID2String</class>
+        <class>org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey</class>
+        <class>org.onap.policy.apex.model.basicmodel.concepts.AxConcept</class>
+        <class>org.onap.policy.apex.model.basicmodel.concepts.AxKeyInfo</class>
+        <class>org.onap.policy.apex.model.basicmodel.concepts.AxKeyInformation</class>
+        <class>org.onap.policy.apex.model.basicmodel.concepts.AxModel</class>
+        <class>org.onap.policy.apex.model.basicmodel.concepts.TestEntity</class>
+        <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextSchema</class>
+        <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextSchemas</class>
+        <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbum</class>
+        <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbums</class>
+        <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextModel</class>
+        <class>org.onap.policy.apex.model.eventmodel.concepts.AxField</class>
+        <class>org.onap.policy.apex.model.eventmodel.concepts.AxInputField</class>
+        <class>org.onap.policy.apex.model.eventmodel.concepts.AxOutputField</class>
+        <class>org.onap.policy.apex.model.eventmodel.concepts.AxEvent</class>
+        <class>org.onap.policy.apex.model.eventmodel.concepts.AxEvents</class>
+        <class>org.onap.policy.apex.model.eventmodel.concepts.AxEventModel</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxLogic</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxTaskParameter</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxTaskLogic</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxTask</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxTasks</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxTaskSelectionLogic</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxStateFinalizerLogic</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxStateOutput</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxStateTaskReference</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxState</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxPolicy</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxPolicies</class>
+        <class>org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel</class>
+
+        <properties>
+            <property name="javax.persistence.jdbc.url" value="jdbc:derby:memory:apex_test" />
+            <property name="javax.persistence.target-database" value="Derby" />
+            <property name="javax.persistence.jdbc.driver" value="org.apache.derby.jdbc.EmbeddedDriver" />
+
+            <property name="eclipselink.ddl-generation" value="drop-and-create-tables" />
+            <property name="eclipselink.ddl-generation.output-mode" value="database" />
+            <property name="eclipselink.logging.level" value="INFO" />
+        </properties>
+    </persistence-unit>
+</persistence>