diff --git a/AMOEBAonAMAK/src/agents/context/Context.java b/AMOEBAonAMAK/src/agents/context/Context.java
index aeb93fd4c076c7ae331d2a7f8de9f8d5053ab8f5..d5bf2f26136b180a68a41c180b68d4be4ddde87a 100644
--- a/AMOEBAonAMAK/src/agents/context/Context.java
+++ b/AMOEBAonAMAK/src/agents/context/Context.java
@@ -111,7 +111,7 @@ public class Context extends AmoebaAgent {
Experiment firstPoint = new Experiment(this);
ArrayList<Percept> var = getAmas().getPercepts();
for (Percept p : var) {
- Range r;
+ Range r = null;
//Pair<Double, Double> radiuses = getAmas().getHeadAgent().getMaxRadiusesForContextCreation(p);
//TODO use neihbors sizes to define radiuses for creation !!!!!!!!!!!
@@ -120,14 +120,33 @@ public class Context extends AmoebaAgent {
- if(getAmas().getHeadAgent().activatedNeighborsContexts.size()>0) {
- double radiusCreation = getAmas().getHeadAgent().minNeighborhoodRadius;
- //double radiusCreation = getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p);
- //double radiusCreation = getAmas().getHeadAgent().minMeanNeighborhoodRaduises;
- getAmas().getEnvironment()
- .trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by mean", this.getName(), p.getName(), getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p).toString())));
- r = new Range(this, p.getValue() - radiusCreation, p.getValue() + radiusCreation, 0, true, true, p, getAmas().getHeadAgent().minMeanNeighborhoodStartIncrements, getAmas().getHeadAgent().minMeanNeighborhoodEndIncrements);
- }else {
+ if(getAmas().getHeadAgent().activatedNeighborsContexts.size()>0 && getAmas().data.isActiveLearning) {
+
+ if(getAmas().getHeadAgent().lastEndogenousRequest != null) {
+ if(getAmas().getHeadAgent().lastEndogenousRequest.getType() == REQUEST.VOID) {
+ double startRange = getAmas().getHeadAgent().lastEndogenousRequest.getBounds().get(p).getA();
+ double endRange = getAmas().getHeadAgent().lastEndogenousRequest.getBounds().get(p).getB();
+
+ getAmas().getEnvironment()
+ .trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by VOID", this.getName(), p.getName(), getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p).toString())));
+ r = new Range(this, startRange, endRange, 0, true, true, p, getAmas().getHeadAgent().minMeanNeighborhoodStartIncrements, getAmas().getHeadAgent().minMeanNeighborhoodEndIncrements);
+
+ }
+ }
+ if(r==null) {
+ double radiusCreation = getAmas().getHeadAgent().minNeighborhoodRadius;
+ //double radiusCreation = getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p);
+ //double radiusCreation = getAmas().getHeadAgent().minMeanNeighborhoodRaduises;
+ getAmas().getEnvironment()
+ .trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by mean", this.getName(), p.getName(), getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p).toString())));
+ r = new Range(this, p.getValue() - radiusCreation, p.getValue() + radiusCreation, 0, true, true, p, getAmas().getHeadAgent().minMeanNeighborhoodStartIncrements, getAmas().getHeadAgent().minMeanNeighborhoodEndIncrements);
+
+ }
+
+
+
+ }
+ if(r==null){
r = new Range(this, p.getValue() - radiuses.getA(), p.getValue() + radiuses.getB(), 0, true, true, p);
getAmas().getEnvironment()
.trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by init", this.getName(), p.getName(), radiuses.getA().toString())));
@@ -199,25 +218,45 @@ public class Context extends AmoebaAgent {
Experiment firstPoint = new Experiment(this);
ArrayList<Percept> var = getAmas().getPercepts();
for (Percept p : var) {
- Range r;
+ Range r = null;
//Pair<Double, Double> radiuses = getAmas().getHeadAgent().getMaxRadiusesForContextCreation(v);
//TODO use neihbors sizes to define radiuses for creation !!!!!!!!!!!
Pair<Double, Double> radiuses = getAmas().getHeadAgent().getRadiusesForContextCreation(p);
- if(getAmas().getHeadAgent().activatedNeighborsContexts.size()>0) {
- double radiusCreation = getAmas().getHeadAgent().minNeighborhoodRadius;
- //double radiusCreation = getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p);
- //double radiusCreation = getAmas().getHeadAgent().minMeanNeighborhoodRaduises;
- getAmas().getEnvironment()
- .trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by mean", this.getName(), p.getName(), getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p).toString())));
- r = new Range(this, p.getValue() - radiusCreation, p.getValue() + radiusCreation, 0, true, true, p, getAmas().getHeadAgent().minMeanNeighborhoodStartIncrements, getAmas().getHeadAgent().minMeanNeighborhoodEndIncrements);
- }else {
+ if(getAmas().getHeadAgent().activatedNeighborsContexts.size()>0 && getAmas().data.isActiveLearning) {
+
+
+
+ if(getAmas().getHeadAgent().lastEndogenousRequest != null) {
+ if(getAmas().getHeadAgent().lastEndogenousRequest.getType() == REQUEST.VOID) {
+ double startRange = getAmas().getHeadAgent().lastEndogenousRequest.getBounds().get(p).getA();
+ double endRange = getAmas().getHeadAgent().lastEndogenousRequest.getBounds().get(p).getB();
+ System.out.println(startRange + " " + endRange);
+ getAmas().getEnvironment()
+ .trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by VOID", this.getName(), p.getName(), getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p).toString())));
+ r = new Range(this, startRange, endRange, 0, true, true, p, getAmas().getHeadAgent().minMeanNeighborhoodStartIncrements, getAmas().getHeadAgent().minMeanNeighborhoodEndIncrements);
+
+ }
+ }
+ if(r==null) {
+ double radiusCreation = getAmas().getHeadAgent().minNeighborhoodRadius;
+ //double radiusCreation = getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p);
+ //double radiusCreation = getAmas().getHeadAgent().minMeanNeighborhoodRaduises;
+ getAmas().getEnvironment()
+ .trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by mean", this.getName(), p.getName(), getAmas().getHeadAgent().meanNeighborhoodRaduises.get(p).toString())));
+ r = new Range(this, p.getValue() - radiusCreation, p.getValue() + radiusCreation, 0, true, true, p, getAmas().getHeadAgent().minMeanNeighborhoodStartIncrements, getAmas().getHeadAgent().minMeanNeighborhoodEndIncrements);
+
+ }
+
+ }
+ if(r==null) {
r = new Range(this, p.getValue() - radiuses.getA(), p.getValue() + radiuses.getB(), 0, true, true, p);
getAmas().getEnvironment()
.trace(TRACE_LEVEL.INFORM, new ArrayList<String>(Arrays.asList("Range creation by init", this.getName(), p.getName(), radiuses.getA().toString())));
}
+
ranges.put(p, r);
ranges.get(p).setValue(p.getValue());
@@ -811,6 +850,7 @@ public class Context extends AmoebaAgent {
}
getEnvironment().trace(TRACE_LEVEL.EVENT,new ArrayList<String>(Arrays.asList("NEW ENDO REQUEST","10", ""+request, ""+this.getName())));
getAmas().getHeadAgent().addSelfRequest(request, 10,this);
+
}
private Double getRandomValueInRange(Percept pct) {
diff --git a/AMOEBAonAMAK/src/agents/head/EndogenousRequest.java b/AMOEBAonAMAK/src/agents/head/EndogenousRequest.java
index e6c7ea98015042a6cc07c5e84d199b729cb16343..599f41ebe8967811363e03d149e0fef7cf5b1153 100644
--- a/AMOEBAonAMAK/src/agents/head/EndogenousRequest.java
+++ b/AMOEBAonAMAK/src/agents/head/EndogenousRequest.java
@@ -97,6 +97,10 @@ public class EndogenousRequest {
return requestType;
}
+ public HashMap<Percept, Pair<Double, Double>> getBounds(){
+ return bounds;
+ }
+
}
diff --git a/AMOEBAonAMAK/src/agents/head/Head.java b/AMOEBAonAMAK/src/agents/head/Head.java
index 1a0c84121e30d7b5369124ee08921b720cb7ca42..ec4984207c658bc87acb97f65c1a91481707abf6 100644
--- a/AMOEBAonAMAK/src/agents/head/Head.java
+++ b/AMOEBAonAMAK/src/agents/head/Head.java
@@ -55,6 +55,7 @@ public class Head extends AmoebaAgent {
public Double minNeighborhoodStartIncrement = null;
public Double minNeighborhoodEndIncrement = null;
+ public EndogenousRequest lastEndogenousRequest = null;
Queue<EndogenousRequest> endogenousRequests = new PriorityQueue<EndogenousRequest>(new Comparator<EndogenousRequest>(){
public int compare(EndogenousRequest r1, EndogenousRequest r2) {
@@ -405,6 +406,11 @@ public class Head extends AmoebaAgent {
getAmas().data.executionTimes[7]=System.currentTimeMillis();
+
+ if(isSelfRequest()) {
+ getAmas().data.activeLearning = true;
+ }
+
criticalities.addCriticality("spatialCriticality",
(getMinMaxVolume() - getVolumeOfAllContexts()) / getMinMaxVolume());
@@ -972,19 +978,23 @@ public class Head extends AmoebaAgent {
private void NCSDetection_ChildContext() {
- getEnvironment().trace(TRACE_LEVEL.DEBUG, new ArrayList<String>(Arrays.asList("------------------------------------------------------------------------------------"
- + "---------------------------------------- NCS DETECTION CHILD CONTEXT")));
-
- if(bestContext!=null) {
- if(!bestContext.getLocalModel().finishedFirstExperiments() && getAmas().data.firstContext && getAmas().getCycle()>0 && !bestContext.isDying()) {
- bestContext.solveNCS_ChildContext();
-
-
+ if(getAmas().data.isActiveLearning) {
+ getEnvironment().trace(TRACE_LEVEL.DEBUG, new ArrayList<String>(Arrays.asList("------------------------------------------------------------------------------------"
+ + "---------------------------------------- NCS DETECTION CHILD CONTEXT")));
+
+ if(bestContext!=null) {
+ if(!bestContext.getLocalModel().finishedFirstExperiments() && getAmas().data.firstContext && getAmas().getCycle()>0 && !bestContext.isDying()) {
+ bestContext.solveNCS_ChildContext();
+
+
+ }
}
}
+
+
}
@@ -1184,28 +1194,33 @@ public class Head extends AmoebaAgent {
private void NCSDetection_PotentialRequest() {
- getEnvironment().trace(TRACE_LEVEL.DEBUG, new ArrayList<String>(Arrays.asList("------------------------------------------------------------------------------------"
- + "---------------------------------------- NCS DETECTION POTENTIAL REQUESTS")));
-
- if (activatedNeighborsContexts.size() > 1) {
- int i = 1;
- for (Context ctxt : activatedNeighborsContexts) {
- for (Context otherCtxt : activatedNeighborsContexts.subList(i, activatedNeighborsContexts.size())) {
- if(!this.isDying() && !ctxt.isDying()) {
- EndogenousRequest potentialRequest = ctxt.endogenousRequest(otherCtxt);
- if(potentialRequest != null) {
- addEndogenousRequest(potentialRequest);
+ if(getAmas().data.isActiveLearning) {
+ getEnvironment().trace(TRACE_LEVEL.DEBUG, new ArrayList<String>(Arrays.asList("------------------------------------------------------------------------------------"
+ + "---------------------------------------- NCS DETECTION POTENTIAL REQUESTS")));
+
+ if (activatedNeighborsContexts.size() > 1) {
+ int i = 1;
+ for (Context ctxt : activatedNeighborsContexts) {
+ for (Context otherCtxt : activatedNeighborsContexts.subList(i, activatedNeighborsContexts.size())) {
+ if(!this.isDying() && !ctxt.isDying()) {
+ EndogenousRequest potentialRequest = ctxt.endogenousRequest(otherCtxt);
+ if(potentialRequest != null) {
+ getAmas().data.activeLearning = true;
+ addEndogenousRequest(potentialRequest);
+ }
}
}
+ i++;
}
- i++;
+ }
+
+ getEnvironment().trace(TRACE_LEVEL.STATE, new ArrayList<String>(Arrays.asList("ENDO REQUESTS", ""+endogenousRequests.size())));
+ for(EndogenousRequest endoRequest : endogenousRequests) {
+ getEnvironment().trace(TRACE_LEVEL.STATE, new ArrayList<String>(Arrays.asList("" + endoRequest)));
}
}
- getEnvironment().trace(TRACE_LEVEL.STATE, new ArrayList<String>(Arrays.asList("ENDO REQUESTS", ""+endogenousRequests.size())));
- for(EndogenousRequest endoRequest : endogenousRequests) {
- getEnvironment().trace(TRACE_LEVEL.STATE, new ArrayList<String>(Arrays.asList("" + endoRequest)));
- }
+
}
@@ -1496,6 +1511,7 @@ public class Head extends AmoebaAgent {
getAmas().data.firstContext = true;
}
+ resetLastEndogenousRequest();
return context;
}
@@ -2170,7 +2186,11 @@ public class Head extends AmoebaAgent {
public boolean isActiveLearning() {
- return isSelfRequest();
+ return isSelfRequest() && getAmas().data.activeLearning;
+ }
+
+ public boolean isSelfLearning() {
+ return isSelfRequest() && getAmas().data.selfLearning;
}
@@ -2178,12 +2198,22 @@ public class Head extends AmoebaAgent {
public HashMap<Percept, Double> getSelfRequest(){
getEnvironment().trace(TRACE_LEVEL.EVENT, new ArrayList<String>(Arrays.asList("FUTURE ACTIVE LEARNING", ""+endogenousRequests.element())));
EndogenousRequest futureRequest = endogenousRequests.poll();
+ lastEndogenousRequest = futureRequest;
for(Context ctxt : futureRequest.getAskingContexts()) {
ctxt.deleteWaitingRequest(futureRequest);
}
+
return futureRequest.getRequest();
}
+ public EndogenousRequest getLastEndogenousRequest() {
+ return lastEndogenousRequest;
+ }
+
+ public void resetLastEndogenousRequest() {
+ lastEndogenousRequest = null;
+ }
+
public void deleteRequest(Context ctxt) {
}
@@ -2194,6 +2224,7 @@ public class Head extends AmoebaAgent {
public void addSelfRequest(HashMap<Percept, Double> request, int priority, Context ctxt){
+ getAmas().data.activeLearning = true;
addEndogenousRequest(new EndogenousRequest(request, null, priority,new ArrayList<Context>(Arrays.asList(ctxt)), REQUEST.SELF));
}
diff --git a/AMOEBAonAMAK/src/agents/percept/Percept.java b/AMOEBAonAMAK/src/agents/percept/Percept.java
index eaa2e3173840ae973d0e0be823418e94b501d757..7bb940b25a64554e3644babd5ff984a21880baab 100644
--- a/AMOEBAonAMAK/src/agents/percept/Percept.java
+++ b/AMOEBAonAMAK/src/agents/percept/Percept.java
@@ -396,7 +396,7 @@ public class Percept extends AmoebaAgent {
}
public double getMappingErrorAllowedOverMapping() {
- return getMinMaxDistance() * getEnvironment().getMappingErrorAllowed() * 1.0;
+ return getMinMaxDistance() * getEnvironment().getMappingErrorAllowed() * 0.5;
}
public double getMappingErrorAllowedMax() {
diff --git a/AMOEBAonAMAK/src/experiments/F_XY_System.java b/AMOEBAonAMAK/src/experiments/F_XY_System.java
index 0a15425a9c996d2cf9188a8dbc1c583b86691d66..42897851276311ddec61ba99286aa73334231878 100644
--- a/AMOEBAonAMAK/src/experiments/F_XY_System.java
+++ b/AMOEBAonAMAK/src/experiments/F_XY_System.java
@@ -101,4 +101,10 @@ public class F_XY_System implements StudiedSystem {
// TODO Auto-generated method stub
}
+
+ @Override
+ public void setSelfLearning(boolean value) {
+ // TODO Auto-generated method stub
+
+ }
}
\ No newline at end of file
diff --git a/AMOEBAonAMAK/src/experiments/benchmark/NDimCube.java b/AMOEBAonAMAK/src/experiments/benchmark/NDimCube.java
index 86c4ca726342fc1d2ef7157836142891b2556fc9..25e9bfc9ad8b04a2d47a7723424a98796dfa0de6 100644
--- a/AMOEBAonAMAK/src/experiments/benchmark/NDimCube.java
+++ b/AMOEBAonAMAK/src/experiments/benchmark/NDimCube.java
@@ -148,4 +148,10 @@ public class NDimCube implements StudiedSystem{
// TODO Auto-generated method stub
}
+
+ @Override
+ public void setSelfLearning(boolean value) {
+ // TODO Auto-generated method stub
+
+ }
}
\ No newline at end of file
diff --git a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
index 3c0ec95825723b5a2eb231d653e20edd38585f97..427434b0d473503205fd6dbf2d7bb42f8e14b0af 100644
--- a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
+++ b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
@@ -41,7 +41,7 @@ public class F_N_Launcher extends Application implements Serializable {
public static double mappingErrorAllowed = 0.03; // MULTI
public static final double explorationIncrement = 1.0 ;
public static final double explorationWidht = 0.5 ;
-
+ public static final boolean setActiveLearning = true ;
public static final int nbCycle = 1000;
@@ -81,6 +81,7 @@ public class F_N_Launcher extends Application implements Serializable {
amoeba.setRenderUpdate(true);
amoeba.data.learningSpeed = learningSpeed;
amoeba.data.numberOfPointsForRegression = regressionPoints;
+ amoeba.data.isActiveLearning = setActiveLearning;
amoeba.getEnvironment().setMappingErrorAllowed(mappingErrorAllowed);
diff --git a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java
index 968cac30920cc02e6018c124babb7807e8c64cfd..acfcc27f01967090b26d12b9092fea0115b662eb 100644
--- a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java
+++ b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java
@@ -41,6 +41,7 @@ public class F_N_Manager implements StudiedSystem{
HashMap<String,Double> selfRequest;
boolean activeLearning = false;
+ boolean selfLearning = false;
double noiseRange;
@@ -153,6 +154,11 @@ public class F_N_Manager implements StudiedSystem{
nonRandomExplorationStep();
+ }
+ else if(selfLearning) {
+
+ selfLearning = false;
+
}
else if(activeLearning) {
@@ -691,6 +697,11 @@ private double[] subZoneCenter3D(int nb) {
activeLearning = value;
}
+ @Override
+ public void setSelfLearning(boolean value) {
+ selfLearning = value;
+ }
+
@Override
public void setSelfRequest(HashMap<Percept, Double> request){
HashMap<String,Double> newRequest = new HashMap<String,Double>();
diff --git a/AMOEBAonAMAK/src/experiments/reinforcement/WithStudiedSystem/ReinforcementManager2D.java b/AMOEBAonAMAK/src/experiments/reinforcement/WithStudiedSystem/ReinforcementManager2D.java
index 1a2788d1de53db70fe80156c48509c4388674c53..56f3dc2dac3ffd382b692002544eafcf8775d770 100644
--- a/AMOEBAonAMAK/src/experiments/reinforcement/WithStudiedSystem/ReinforcementManager2D.java
+++ b/AMOEBAonAMAK/src/experiments/reinforcement/WithStudiedSystem/ReinforcementManager2D.java
@@ -512,6 +512,17 @@ public class ReinforcementManager2D implements StudiedSystem{
}
+
+
+
+
+
+ @Override
+ public void setSelfLearning(boolean value) {
+ // TODO Auto-generated method stub
+
+ }
+
diff --git a/AMOEBAonAMAK/src/kernel/AMOEBA.java b/AMOEBAonAMAK/src/kernel/AMOEBA.java
index 866b77c0947fd938b4c5ef417dda4addf5b97d62..d246a9e00f672798511c0ab6b19ca606617c451f 100644
--- a/AMOEBAonAMAK/src/kernel/AMOEBA.java
+++ b/AMOEBAonAMAK/src/kernel/AMOEBA.java
@@ -248,10 +248,16 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
if(studiedSystem != null) {
if(head.isActiveLearning()) {
+ data.activeLearning = false;
studiedSystem.setActiveLearning(true);
studiedSystem.setSelfRequest(head.getSelfRequest());
}
+ else if(head.isSelfLearning()) {
+ data.selfLearning = false;
+ studiedSystem.setSelfLearning(true);
+ studiedSystem.setSelfRequest(head.getSelfRequest());
+ }
}
super.onSystemCycleEnd();
diff --git a/AMOEBAonAMAK/src/kernel/AmoebaData.java b/AMOEBAonAMAK/src/kernel/AmoebaData.java
index 449f40008954bd32bf04b3b46e72a301638d57dc..6e32b074e225279bc62f5394dca51a0487470a37 100644
--- a/AMOEBAonAMAK/src/kernel/AmoebaData.java
+++ b/AMOEBAonAMAK/src/kernel/AmoebaData.java
@@ -46,7 +46,10 @@ public class AmoebaData implements Serializable {
public boolean newContextWasCreated = false;
public boolean contextFromPropositionWasSelected = false;
+ public boolean isActiveLearning;
+
public boolean activeLearning = false;
+ public boolean selfLearning = false;
public HashMap<String, Double> selfRequest;
diff --git a/AMOEBAonAMAK/src/kernel/StudiedSystem.java b/AMOEBAonAMAK/src/kernel/StudiedSystem.java
index b233951eaf945ee789545b3325e8ce0924bcda40..88ec60c66a966bc527bfc14404444a68c96d08fd 100644
--- a/AMOEBAonAMAK/src/kernel/StudiedSystem.java
+++ b/AMOEBAonAMAK/src/kernel/StudiedSystem.java
@@ -37,7 +37,7 @@ public interface StudiedSystem extends Serializable{
public double requestOracle(HashMap<String, Double> request);
public void setActiveLearning(boolean value);
-
+ public void setSelfLearning(boolean value);
public void setSelfRequest(HashMap<Percept, Double> request);