SUB: code submited to AAAI

AMAKFX/src/multiagent/framework/ui/VUIMulti.java

@@ -4,7 +4,7 @@ import java.util.*;
 import java.util.concurrent.locks.ReentrantLock;
 
 import multiagent.framework.tools.RunLaterHelper;
-import fr.irit.smac.amak.ui.drawables.*;
+import multiagent.framework.ui.drawables.*;
 import javafx.event.ActionEvent;
 import javafx.event.EventHandler;
 import javafx.geometry.Insets;

ELLSAonAMAK/src/experiments/roboticArm/launchers/RobotLaunchExampleOneExperimentationWithArgs.java

@@ -36,7 +36,6 @@ public class RobotLaunchExampleOneExperimentationWithArgs {
         PARAMS.nbRequestCycle = Integer.parseInt(args[2]);
         PARAMS.nbEpisodes = Integer.parseInt(args[3]);
         PARAMS.mappingErrorAllowed = ((double)Integer.parseInt(args[4]))/100;
-        System.out.println(PARAMS.mappingErrorAllowed);
         PARAMS.neighborhoodMultiplicator = Integer.parseInt(args[5]);
         PARAMS.extendedArmLength = Integer.parseInt(args[6]);
 
@@ -108,43 +107,40 @@ public class RobotLaunchExampleOneExperimentationWithArgs {
         for (String dataName : dataStrings){
             OptionalDouble averageScore = data.get(dataName).stream().mapToDouble(a->a).average();
             Double deviationScore = data.get(dataName).stream().mapToDouble(a->Math.pow((a-averageScore.getAsDouble()),2)).sum();
-            if(averageScore.getAsDouble()<1){
-                xpCSV.write(new ArrayList<>(Arrays.asList(dataName ,"AVERAGE" ,averageScore.getAsDouble()*100+"" ,"DEVIATION","" + 100*Math.sqrt(deviationScore/data.get(dataName).size()))));
-            }else{
-                xpCSV.write(new ArrayList<>(Arrays.asList(dataName ,"AVERAGE" ,averageScore.getAsDouble()+"" ,"DEVIATION","" + Math.sqrt(deviationScore/data.get(dataName).size()))));
-            }
+
 
         }
 
-        xpCSV.write(new ArrayList<>(Arrays.asList(" ")));
+        OptionalDouble averageScoreEndo = data.get("endogenousLearningSituations").stream().mapToDouble(a->a).average();
+        Double deviationScoreEndo = data.get("endogenousLearningSituations").stream().mapToDouble(a->Math.pow((a-averageScoreEndo.getAsDouble()),2)).sum();
+        xpCSV.write(new ArrayList<>(Arrays.asList("endogenousLearningSituations" ,"AVERAGE" ,averageScoreEndo.getAsDouble()+"" ,"DEVIATION","" + Math.sqrt(deviationScoreEndo/data.get("endogenousLearningSituations").size()))));
+
 
         //Create the formatter for round the values of scores
         Locale currentLocale = Locale.getDefault();
         DecimalFormatSymbols otherSymbols = new DecimalFormatSymbols(currentLocale);
         otherSymbols.setDecimalSeparator('.');
         DecimalFormat df = new DecimalFormat("##.##", otherSymbols);
-        xpCSV.write(new ArrayList<>(Arrays.asList("ROUNDED")));
+
         for (String dataName : dataStrings){
             OptionalDouble averageScore = data.get(dataName).stream().mapToDouble(a->a).average();
             Double deviationScore = data.get(dataName).stream().mapToDouble(a->Math.pow((a-averageScore.getAsDouble()),2)).sum();
-            if(averageScore.getAsDouble()<1){
-                xpCSV.write(new ArrayList<>(Arrays.asList(dataName ,"AVERAGE" ,df.format(averageScore.getAsDouble()*100)+"" ,"DEVIATION","" + df.format(100*Math.sqrt(deviationScore/data.get(dataName).size())))));
-            }
+
 
 
         }
-        xpCSV.write(new ArrayList<>(Arrays.asList(" ")));
+
 
         for (String dataName : dataStrings){
             OptionalDouble averageScore = data.get(dataName).stream().mapToDouble(a->a).average();
             Double deviationScore = data.get(dataName).stream().mapToDouble(a->Math.pow((a-averageScore.getAsDouble()),2)).sum();
-            if(averageScore.getAsDouble()>=1){
-                xpCSV.write(new ArrayList<>(Arrays.asList(dataName ,"AVERAGE" ,df.format(averageScore.getAsDouble())+"" ,"DEVIATION","" + df.format(Math.sqrt(deviationScore/data.get(dataName).size())))));
-            }
+
 
 
         }
 
+
+
         xpCSV.write(new ArrayList<>(Arrays.asList(" ")));
 
         OptionalDouble averageScore = data.get("prediction").stream().mapToDouble(a->a).average();
@@ -153,12 +149,12 @@ public class RobotLaunchExampleOneExperimentationWithArgs {
         Double deviationScoreDisp = data.get("predictionDisp").stream().mapToDouble(a->Math.pow((a-averageScoreDisp.getAsDouble()),2)).sum();
 
 
-        xpCSV.write(new ArrayList<>(Arrays.asList("PREDICTION AVERAGE" , ""+averageScore.getAsDouble() ,"DEVIATION" ,""+Math.sqrt(deviationScore/data.get("prediction").size()))));
-        xpCSV.write(new ArrayList<>(Arrays.asList("DISPERSION AVERAGE" , ""+averageScoreDisp.getAsDouble() ,"DEVIATION" ,""+Math.sqrt(deviationScoreDisp/data.get("predictionDisp").size()))));
+        xpCSV.write(new ArrayList<>(Arrays.asList("GOAL ERROR AVERAGE" , ""+averageScore.getAsDouble() ,"DEVIATION" ,""+Math.sqrt(deviationScore/data.get("prediction").size()))));
+        xpCSV.write(new ArrayList<>(Arrays.asList("DEVIATION AVERAGE" , ""+averageScoreDisp.getAsDouble() ,"DEVIATION" ,""+Math.sqrt(deviationScoreDisp/data.get("predictionDisp").size()))));
 
 
-        xpCSV.write(new ArrayList<>(Arrays.asList("PREDICTION AVERAGE %" , ""+df.format(100*averageScore.getAsDouble()) ,"DEVIATION %" ,""+df.format(100*Math.sqrt(deviationScore/data.get("prediction").size())))));
-        xpCSV.write(new ArrayList<>(Arrays.asList("DISPERSION AVERAGE %" , ""+df.format(100*averageScoreDisp.getAsDouble()) ,"DEVIATION %" ,""+df.format(100*Math.sqrt(deviationScoreDisp/data.get("predictionDisp").size())))));
+        xpCSV.write(new ArrayList<>(Arrays.asList("GOAL ERROR AVERAGE %" , ""+df.format(100*averageScore.getAsDouble()) ,"DEVIATION %" ,""+df.format(100*Math.sqrt(deviationScore/data.get("prediction").size())))));
+        xpCSV.write(new ArrayList<>(Arrays.asList("DEVIATION AVERAGE %" , ""+df.format(100*averageScoreDisp.getAsDouble()) ,"DEVIATION %" ,""+df.format(100*Math.sqrt(deviationScoreDisp/data.get("predictionDisp").size())))));
 
         xpCSV.close();
 

ELLSAonAMAK/src/experiments/roboticArm/launchers/RobotLaunchExampleWithUIWithArgs.java

@@ -34,7 +34,6 @@ public class RobotLaunchExampleWithUIWithArgs extends Application{
         PARAMS.nbTrainingCycle = Integer.parseInt(args[1]);
         PARAMS.nbRequestCycle = Integer.parseInt(args[2]);
         PARAMS.mappingErrorAllowed = ((double)Integer.parseInt(args[3]))/100;
-            System.out.println(PARAMS.mappingErrorAllowed);
         PARAMS.neighborhoodMultiplicator = Integer.parseInt(args[4]);
         PARAMS.extendedArmLength = (double)Integer.parseInt(args[5]);
 

ELLSAonAMAK/src/experiments/roboticArm/simulation/RobotArmManager.java

@@ -388,7 +388,7 @@ public class RobotArmManager {
                 }
 
             }else{
-                if(requestCycle%50==0)  TRACE.print(TRACE_LEVEL.SUBCYCLE,"REQUEST [" + requestCycle + "] ");
+                if(requestCycle%50==0)  TRACE.print(TRACE_LEVEL.SUBCYCLE,"EXPLOITATION [" + requestCycle + "] ");
 
                 goalAngles = request(angles, poseGoal, cycle);
 
@@ -438,7 +438,7 @@ public class RobotArmManager {
             }
         }else{
             finished = true;
-            TRACE.print(TRACE_LEVEL.SUBCYCLE,"ERROR: "+averageError.getAsDouble() + " [ DISPERSION: " + Math.sqrt(errorDispersion/allGoalErrors.size()) + " ]");
+            TRACE.print(TRACE_LEVEL.SUBCYCLE,"GOAL ERROR: "+averageError.getAsDouble() + " [ STANDARD DEVIATION: " + Math.sqrt(errorDispersion/allGoalErrors.size()) + " ]");
             for (int i = 0;i<jointsNb;i++){
 
                 controller.setJoint(i,angles,0.0);

ELLSAonAMAK/src/kernel/ELLSA.java

@@ -144,7 +144,6 @@ public class ELLSA extends Amas<World> implements IELLSA {
 		if(amasMultiUIWindow!=null) {
 
 			if(getCycle()==1){
-				System.out.println(getPercepts().size());
 				for(int i =0;i<getPercepts().size();i++){
 					multiUIWindow.VUInDimensions.createAndAddString(10, 10 + getPercepts().size()*10 - 10*i,getPercepts().get(i).getName()).setFixed().setShowInExplorer(false);
 				}

documentation/README.md

+# Running Experimentations
+
+## Prerequisites
+
+Install java JRE 8
+https://docs.oracle.com/javase/8/docs/technotes/guides/install/install_overview.html
+
+
+## Allowed parameters
+
+number_of_joints: `2, 3, 6, 10, 20, 30`
+
+learning_situations: `any positive integer (1000 is used in the paper)` 
+
+exploitation_situations: `any positive integer (200 is used in the paper)`
+
+number_of_episodes: `any positive integer (15 is used in the paper)`
+
+precision_range (%): `any positive integer between 1 and 10 (1 and 3 are used in the paper)`
+
+neighborhood_size: `2, 4, 6, 8, 10, 12, 14, 16`
+
+arm_length: `any positive integer (50 is used in the paper)`
+
+## Run experimentation with UI
+
+```
+java -jar experimentationWithUi.jar <number_of_joints> <learning_situations> <exploitation_situations> <precision_range> <neighborhood_size> <arm_length>
+```
+
+Example: ``-jar experimentationWithUi.jar 2 1000 200 3 2 50``
+
+To start the learning, select the speed on the bottom left of the `Robot Arm Simulation` window. Step by step running is also possible. You can see the Context Agents by clicking on the button `Allow Rendering` in the `Multi-Agent System` window. ATTENTION: do no use the speed selection on the `Multi-Agent System` window.
+
+During exploitation, one simulation step shows the target goal and the next simulation step shows the result of the exploitation.
+
+After the exploitation is done, the goal error is displayed on the console and you can set the speed to `Stop` or close the windows.
+
+
+
+## Run experimentation with several learning episodes
+
+```
+java -jar experimentation.jar <number_of_joints> <learning_situations> <exploitation_situations> <number_of_episodes> <precision_range> <neighborhood_size> <arm_length>
+```
+Example: ``-jar experimentation.jar 2 1000 200 15 3 2 50``
+