public ClarionAgent(WSProxy nws, String creature_ID, String creature_Name)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
mind = new MindViewer();
mind.Show();
creatureId = creature_ID;
creatureName = creature_Name;
// Initialize Input Information
inputWallAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_WALL_AHEAD);
inputFoodAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AHEAD);
inputJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_AHEAD);
inputNeedJewel = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_NEED_JEWEL);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
outputGoAhead = World.NewExternalActionChunk(CreatureActions.GO_AHEAD.ToString());
outputGetJewel = World.NewExternalActionChunk(CreatureActions.GET_JEWEL.ToString());
outputEatFood = World.NewExternalActionChunk(CreatureActions.EAT_FOOD.ToString());
outputGoToJewel = World.NewExternalActionChunk(CreatureActions.GO_TO_JEWEL.ToString());
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
Console.WriteLine("Agent started");
}
public ClarionAgent(WSProxy nws, String creature_ID, String creature_Name)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
mind = new MindViewer();
mind.Show();
creatureId = creature_ID;
creatureName = creature_Name;
// Initialize Input Information
inputWallAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_WALL_AHEAD);
inputFoodAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AHEAD);
inputJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_AHEAD);
inputDistantJewel = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_DISTANT_JEWEL);
inputDistantFood = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_DISTANT_FOOD);
inputDeliverLeaflet = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_DELIVER_LEAFLET);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
//outputGoAhead = World.NewExternalActionChunk(CreatureActions.GO_AHEAD.ToString());
outputGoToClosestJewel = World.NewExternalActionChunk(CreatureActions.GO_TO_CLOSEST_JEWEL.ToString());
outputGoToClosestFood = World.NewExternalActionChunk(CreatureActions.GO_TO_CLOSEST_FOOD.ToString());
outputEatFood = World.NewExternalActionChunk(CreatureActions.EAT_FOOD.ToString());
outputSackJewel = World.NewExternalActionChunk(CreatureActions.SACK_JEWEL.ToString());
outputDeliverLeaflet = World.NewExternalActionChunk(CreatureActions.PREPARE_TO_DELIVER_LEAFLET.ToString());
outputStop = World.NewExternalActionChunk(CreatureActions.STOP.ToString());
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
Console.WriteLine("Agent started");
}
public ClarionAgent(WorldServer nws, String creature_ID, String creature_Name)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
//mind = new Mind();
//mind.Show();
creatureId = creature_ID;
creatureName = creature_Name;
// Initialize Input Information
inputWallAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_WALL_AHEAD);
inputFoodAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AHEAD);
inputLeafletJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_LEAFLET_JEWEL_AHEAD);
inputNonLeafletJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_NON_LEAFLET_JEWEL_AHEAD);
inputCloseObjectAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_CLOSE_OBJECT_AHEAD);
inputHasCompletedLeaflet = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_HAS_COMPLETED_LEAFLET);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
outputGoAhead = World.NewExternalActionChunk(CreatureActions.GO_AHEAD.ToString());
outputEat = World.NewExternalActionChunk(CreatureActions.EAT.ToString());
outputHide = World.NewExternalActionChunk(CreatureActions.HIDE.ToString());
outputSack = World.NewExternalActionChunk(CreatureActions.SACK.ToString());
outputStop = World.NewExternalActionChunk(CreatureActions.STOP.ToString());
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
Console.WriteLine("Agent started");
}
static void SetupBPNetwork(Agent reasoner)
{
//Chunks for the whales, tuna, and bears
DeclarativeChunk TunaChunk = World.NewDeclarativeChunk("Tuna");
DeclarativeChunk WhaleChunk = World.NewDeclarativeChunk("Whale");
DeclarativeChunk BearChunk = World.NewDeclarativeChunk("Bear");
//The 2 properties (as DV pairs)
DimensionValuePair livesinwater = World.NewDimensionValuePair("lives in", "water");
DimensionValuePair eatsfish = World.NewDimensionValuePair("eats", "fish");
//The BP network to be used in the bottom level of the NACS
BPNetwork net = AgentInitializer.InitializeAssociativeMemoryNetwork(reasoner, BPNetwork.Factory);
//Adds the properties (as inputs) and chunks (as outputs) to the BP network
net.Input.Add(livesinwater);
net.Input.Add(eatsfish);
net.Output.Add(TunaChunk);
net.Output.Add(WhaleChunk);
net.Output.Add(BearChunk);
reasoner.Commit(net);
//Adds the chunks to the GKS
reasoner.AddKnowledge(TunaChunk);
reasoner.AddKnowledge(WhaleChunk);
reasoner.AddKnowledge(BearChunk);
//Initializes a trainer to use to train the BP network
GenericEquation trainer = ImplicitComponentInitializer.InitializeTrainer(GenericEquation.Factory, (Equation)trainerEQ);
//Adds the properties (as inputs) and chunks (as outputs) to the trainer
trainer.Input.Add(livesinwater);
trainer.Input.Add(eatsfish);
trainer.Output.Add(TunaChunk);
trainer.Output.Add(WhaleChunk);
trainer.Output.Add(BearChunk);
trainer.Commit();
//Sets up data sets for each of the 2 properties
List <ActivationCollection> sis = new List <ActivationCollection>();
ActivationCollection si = ImplicitComponentInitializer.NewDataSet();
si.Add(livesinwater, 1);
sis.Add(si);
si = ImplicitComponentInitializer.NewDataSet();
si.Add(eatsfish, 1);
sis.Add(si);
Console.Write("Training AMN...");
//Trains the BP network to report associative knowledge between the properties and the chunks
ImplicitComponentInitializer.Train(net, trainer, sis, ImplicitComponentInitializer.TrainingTerminationConditions.SUM_SQ_ERROR);
Console.WriteLine("Finished!");
}
public ClarionAgent(WSProxy nws, String creature_ID, String creature_Name, List <Leaflet> leafletList)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
mind = new MindViewer();
mind.Show();
creatureId = creature_ID;
creatureName = creature_Name;
// Initialize Input Information
inputWallCreatureAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_WALL_AHEAD);
inputFoodAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AHEAD);
inputJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_AHEAD);
inputJewelInVision = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_IN_VISION);
inputFuelLow = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FUEL_LOW);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
outputGoAhead = World.NewExternalActionChunk(CreatureActions.GO_AHEAD.ToString());
outputEatFood = World.NewExternalActionChunk(CreatureActions.EAT_FOOD.ToString());
outputGetJewel = World.NewExternalActionChunk(CreatureActions.GET_JEWEL.ToString());
outputHideJewel = World.NewExternalActionChunk(CreatureActions.HIDE_JEWEL.ToString());
outputGoalAchieved = World.NewExternalActionChunk(CreatureActions.GOAL_ACHIEVED.ToString());
outputGoToJewelInVision = World.NewExternalActionChunk(CreatureActions.GO_TO_JEWEL.ToString());
outputFuelLow = World.NewExternalActionChunk(CreatureActions.FUEL_LOW.ToString());
//List<Thing> listThing = nws.SendGetCreatureState (creature_ID);
//IList<Thing> response = null;
//if (worldServer != null && worldServer.IsConnected) {
// response = worldServer.SendGetCreatureState (creatureName);
// Creature cc = (Creature)response [0];
// leaflet1.leafletID = cc.leaflets [0].leafletID;
// leaflet2.leafletID = cc.leaflets [1].leafletID;
// leaflet3.leafletID = cc.leaflets [2].leafletID;
// Console.WriteLine ("Creature found: " + cc.Name);
// Console.WriteLine ("LF1: " + cc.leaflets [0].leafletID);
// Console.WriteLine ("LF2: " + cc.leaflets [1].leafletID);
// Console.WriteLine ("LF3: " + cc.leaflets [2].leafletID);
//}
// Load leaflet control
loadLeafletsControl(leafletList);
mind.loadLeaflet(leafletList[0], leafletList[1], leafletList[2]);
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
//Console.WriteLine ("Agent started");
}
private void Initialize()
{
World.LoggingLevel = TraceLevel.Off;
p1 = World.NewDimensionValuePair("Peg", 1);
p2 = World.NewDimensionValuePair("Peg", 2);
p3 = World.NewDimensionValuePair("Peg", 3);
p4 = World.NewDimensionValuePair("Peg", 4);
p5 = World.NewDimensionValuePair("Peg", 5);
mp1 = World.NewExternalActionChunk();
mp2 = World.NewExternalActionChunk();
mp3 = World.NewExternalActionChunk();
mp4 = World.NewExternalActionChunk();
mp5 = World.NewExternalActionChunk();
mp1 += p1;
mp2 += p2;
mp3 += p3;
mp4 += p4;
mp5 += p5;
John = World.NewAgent();
net = AgentInitializer.InitializeImplicitDecisionNetwork(John, SimplifiedQBPNetwork.Factory);
net.Input.Add(p1);
net.Input.Add(p2);
net.Input.Add(p3);
net.Input.Add(p4);
net.Input.Add(p5);
net.Output.Add(mp1);
net.Output.Add(mp2);
net.Output.Add(mp3);
net.Output.Add(mp4);
net.Output.Add(mp5);
net.Parameters.LEARNING_RATE = 1;
net.Parameters.MOMENTUM = .01;
John.Commit(net);
RefineableActionRule.GlobalParameters.GENERALIZATION_THRESHOLD_1 = -.01;
RefineableActionRule.GlobalParameters.SPECIALIZATION_THRESHOLD_1 = -.4;
}
public void SetNeuralNetworkInputNodes(int NumberOfInputNodes)
{
#region Amino Acid List
List <AminoAcid> AminoAcidList = new List <AminoAcid>();
#region List Entries
AminoAcidList.Add(new AminoAcid("Alanine", 'A'));
AminoAcidList.Add(new AminoAcid("Arginine", 'R'));
AminoAcidList.Add(new AminoAcid("Asparagine", 'N'));
AminoAcidList.Add(new AminoAcid("Aspartic Acid", 'D'));
AminoAcidList.Add(new AminoAcid("Cystein", 'C'));
AminoAcidList.Add(new AminoAcid("Glutamine", 'Q'));
AminoAcidList.Add(new AminoAcid("Glutamic Acid", 'E'));
AminoAcidList.Add(new AminoAcid("Glycine", 'G'));
AminoAcidList.Add(new AminoAcid("Histidine", 'H'));
AminoAcidList.Add(new AminoAcid("Isoleucine", 'I'));
AminoAcidList.Add(new AminoAcid("Leucine", 'L'));
AminoAcidList.Add(new AminoAcid("Lysine", 'K'));
AminoAcidList.Add(new AminoAcid("Methionine", 'M'));
AminoAcidList.Add(new AminoAcid("Phenylalanine", 'F'));
AminoAcidList.Add(new AminoAcid("Proline", 'P'));
AminoAcidList.Add(new AminoAcid("Serine", 'S'));
AminoAcidList.Add(new AminoAcid("Threonine", 'T'));
AminoAcidList.Add(new AminoAcid("Tryptophan", 'W'));
AminoAcidList.Add(new AminoAcid("Tyrosine", 'Y'));
AminoAcidList.Add(new AminoAcid("Valine", 'V'));
AminoAcidList.Add(new AminoAcid("Default", 'X'));
#endregion
#endregion
String NodeID = null;
for (int NetworkNodeCount = 1; NetworkNodeCount <= NumberOfInputNodes; NetworkNodeCount++)
{
NodeID = "NetworkNode_" + NetworkNodeCount;
for (int AminoAcidCount = 0; AminoAcidCount < AminoAcidList.Count; AminoAcidCount++)
{
DimensionValuePair DimValuePair = World.NewDimensionValuePair(NodeID, AminoAcidList.ElementAt(AminoAcidCount).getSymbol());
NeuralNetwork.Input.Add(DimValuePair);
}
}
}
public ClarionAgent(WSProxy nws, String creature_ID, String creature_Name)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
mind = new MindViewer();
mind.Show();
creatureId = creature_ID;
creatureName = creature_Name;
// Initialize Input Information
inputWallCreatureAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_WALL_AHEAD);
inputFoodAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AHEAD);
inputJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_AHEAD);
inputJewelInVision = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_IN_VISION);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
outputGoAhead = World.NewExternalActionChunk(CreatureActions.GO_AHEAD.ToString());
outputEatFood = World.NewExternalActionChunk(CreatureActions.EAT_FOOD.ToString());
outputGetJewel = World.NewExternalActionChunk(CreatureActions.GET_JEWEL.ToString());
outputHideJewel = World.NewExternalActionChunk(CreatureActions.HIDE_JEWEL.ToString());
outputGoalAchieved = World.NewExternalActionChunk(CreatureActions.GOAL_ACHIEVED.ToString());
outputGoToJewelInVision = World.NewExternalActionChunk(CreatureActions.GO_TO_JEWEL.ToString());
/*List<Thing> thingList = nws.SendGetCreatureState (creature_ID);
* foreach(Thing t in thingList) {
* Console.WriteLine ("Thing: ");
* Console.Write (t);
* }*/
// Define jewels out of scope
jewelOutOfScope.Add("Orange");
jewelOutOfScope.Add("DarkGray_Spoiled");
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
Console.WriteLine("Agent started");
}
public double CalculateSupport_MemoryGroup(ActivationCollection si, Rule r = null)
{
DimensionValuePair currentP = (from t in si
where t.WORLD_OBJECT.AsDimensionValuePair.Dimension.ToString() == "Current P" &&
r.GeneralizedCondition.Contains(t.WORLD_OBJECT, true)
select t.WORLD_OBJECT.AsDimensionValuePair).FirstOrDefault();
DimensionValuePair previousW = (from t in si
where t.WORLD_OBJECT is ExternalActionChunk &&
r.GeneralizedCondition.Contains(t.WORLD_OBJECT, true)
select t.WORLD_OBJECT.AsDimensionValuePair).FirstOrDefault();
if (currentP == null || previousW == null)
{
return(0);
}
else
{
return((si[currentP] == John.Parameters.MAX_ACTIVATION && si[previousW] == John.Parameters.MAX_ACTIVATION) ? 1 : 0);
}
}
// To indicate we are done.
//Boolean allJewelsCollected = false;
//Boolean reachedDeliverySpot = false;
#endregion
#region Constructor
public ClarionAgent(WSProxy nws, String creature_ID, String creature_Name)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
mind = new MindViewer();
mind.Show();
creatureId = creature_ID;
creatureName = creature_Name;
// Create a (fake) delivery spot.
// It seems WSProxy.cs does not support it, so place it at (0,0).
deliverySpot = new Thing();
deliverySpot.X1 = 0;
deliverySpot.Y1 = 0;
// Initialize Input Information
inputWallAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_WALL_AHEAD);
inputJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_AHEAD);
inputFoodAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AHEAD);
inputJewelAway = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_AWAY);
inputFoodAway = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AWAY);
inputAllJewelsCollected = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_ALL_JEWELS_COLLECTED);
inputCreatureCanDeliver = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_CREATURE_CAN_DELIVER);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
outputGetJewel = World.NewExternalActionChunk(CreatureActions.GET_JEWEL.ToString());
outputGetFood = World.NewExternalActionChunk(CreatureActions.GET_FOOD.ToString());
outputGoToJewel = World.NewExternalActionChunk(CreatureActions.GO_TO_JEWEL.ToString());
outputGoToFood = World.NewExternalActionChunk(CreatureActions.GO_TO_FOOD.ToString());
outputWander = World.NewExternalActionChunk(CreatureActions.WANDER.ToString());
outputGoToDeliverySpot = World.NewExternalActionChunk(CreatureActions.GO_TO_DELIVER.ToString());
outputDoDelivery = World.NewExternalActionChunk(CreatureActions.DELIVER.ToString());
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
Console.WriteLine("Agent started");
}
/// <summary>
/// Output action that makes the agent go ahead
/// </summary>
#endregion
#endregion
#region Constructor
public ClarionAgent(WorldServer nws, String creature_ID, String creature_Name)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
mind = new Mind();
mind.Show();
creatureId = creature_ID;
creatureName = creature_Name;
// Initialize Input Information
inputAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_AHEAD);
inputGoItem = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_GO_ITEM);
inputSackItItem = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_SACKIT_ITEM);
inputEatItem = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_EAT_ITEM);
inputStopCreature = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_STOP_CREATURE);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
outputGoItem = World.NewExternalActionChunk(CreatureActions.GO_ITEM.ToString());
outputSackItItem = World.NewExternalActionChunk(CreatureActions.SACKIT_ITEM.ToString());
outputEatItem = World.NewExternalActionChunk(CreatureActions.EAT_ITEM.ToString());
outputStopCreature = World.NewExternalActionChunk(CreatureActions.STOP_CREATURE.ToString());
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
Console.WriteLine("Agent started");
}
/// </summary>
/// <param name="nws"></param>
/// <param name="creature_ID"></param>
/// <param name="creature_Name"></param>
#endregion
#region Constructor
public ClarionAgent(WorldServer nws, String creature_ID, String creature_Name)
{
worldServer = nws;
// Initialize the agent
CurrentAgent = World.NewAgent("Current Agent");
// FMT 15/06 - temp disable mind
//mind = new Mind();
//mind.Show ();
creatureId = creature_ID;
creatureName = creature_Name;
// Initialize Input Information
inputWallAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_WALL_AHEAD);
// FMT 29/04/2017
inputFoodAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_FOOD_AHEAD);
inputJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_AHEAD);
inputLeafletJewelAhead = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_LEAFLET_JEWEL_AHEAD);
inputJewelHide = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_JEWEL_HIDE);
inputEnergyLow = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_ENERGY_LOW);
inputCloseObject = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_CLOSE_OBJECT);
inputLeafletComplete = World.NewDimensionValuePair(SENSOR_VISUAL_DIMENSION, DIMENSION_LEAFLET_COMPLETE);
// Initialize Output actions
outputRotateClockwise = World.NewExternalActionChunk(CreatureActions.ROTATE_CLOCKWISE.ToString());
outputGoAhead = World.NewExternalActionChunk(CreatureActions.GO_AHEAD.ToString());
// FMT 29/04/2017
outputEat = World.NewExternalActionChunk(CreatureActions.EAT.ToString());
outputGet = World.NewExternalActionChunk(CreatureActions.GET.ToString());
outputGoTo = World.NewExternalActionChunk(CreatureActions.GO_TO.ToString());
outputHide = World.NewExternalActionChunk(CreatureActions.HIDE.ToString());
outputStop = World.NewExternalActionChunk(CreatureActions.STOP.ToString());
//Create thread to simulation
runThread = new Thread(CognitiveCycle);
Console.WriteLine("Agent started");
}
static void Main(string[] args)
{
//Initialize the task
Console.WriteLine("Initializing the Simple Hello World Task");
int CorrectCounter = 0;
int NumberTrials = 10000;
int progress = 0;
World.LoggingLevel = TraceLevel.Off;
TextWriter orig = Console.Out;
StreamWriter sw = File.CreateText("HelloWorldSimple.txt");
DimensionValuePair hi = World.NewDimensionValuePair("Salutation", "Hellow");
DimensionValuePair bye = World.NewDimensionValuePair("Salutation", "Goodbyew");
ExternalActionChunk sayHi = World.NewExternalActionChunk("Hello");
ExternalActionChunk sayBye = World.NewExternalActionChunk("Goodbye");
//Initialize the Agent
Agent John = World.NewAgent("John");
SimplifiedQBPNetwork net = AgentInitializer.InitializeImplicitDecisionNetwork(John, SimplifiedQBPNetwork.Factory);
net.Input.Add(hi);
net.Input.Add(bye);
net.Output.Add(sayHi);
net.Output.Add(sayBye);
John.Commit(net);
net.Parameters.LEARNING_RATE = 1;
John.ACS.Parameters.PERFORM_RER_REFINEMENT = false;
//Run the task
Console.WriteLine("Running the Simple Hello World Task");
Console.SetOut(sw);
Random rand = new Random();
SensoryInformation si;
ExternalActionChunk chosen;
for (int i = 0; i < NumberTrials; i++)
{
si = World.NewSensoryInformation(John);
//Randomly choose an input to perceive.
if (rand.NextDouble() < .5)
{
//Say "Hello"
si.Add(hi, John.Parameters.MAX_ACTIVATION);
si.Add(bye, John.Parameters.MIN_ACTIVATION);
}
else
{
//Say "Goodbye"
si.Add(hi, John.Parameters.MIN_ACTIVATION);
si.Add(bye, John.Parameters.MAX_ACTIVATION);
}
//Perceive the sensory information
John.Perceive(si);
//Choose an action
chosen = John.GetChosenExternalAction(si);
//Deliver appropriate feedback to the agent
if (chosen == sayHi)
{
//The agent said "Hello".
if (si[hi] == John.Parameters.MAX_ACTIVATION)
{
//The agent responded correctly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was correct");
//Record the agent's success.
CorrectCounter++;
//Give positive feedback.
John.ReceiveFeedback(si, 1.0);
}
else
{
//The agent responded incorrectly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was incorrect");
//Give negative feedback.
John.ReceiveFeedback(si, 0.0);
}
}
else
{
//The agent said "Goodbye".
if (si[bye] == John.Parameters.MAX_ACTIVATION)
{
//The agent responded correctly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was correct");
//Record the agent's success.
CorrectCounter++;
//Give positive feedback.
John.ReceiveFeedback(si, 1.0);
}
else
{
//The agent responded incorrectly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was incorrect");
//Give negative feedback.
John.ReceiveFeedback(si, 0.0);
}
}
Console.SetOut(orig);
progress = (int)(((double)(i + 1) / (double)NumberTrials) * 100);
Console.CursorLeft = 0;
Console.Write(progress + "% Complete..");
Console.SetOut(sw);
}
//Report Results
Console.WriteLine("Reporting Results for the Simple Hello World Task");
Console.WriteLine("John got " + CorrectCounter + " correct out of " + NumberTrials + " trials (" +
(int)Math.Round(((double)CorrectCounter / (double)NumberTrials) * 100) + "%)");
Console.WriteLine("At the end of the task, John had learned the following rules:");
foreach (var i in John.GetInternals(Agent.InternalContainers.ACTION_RULES))
{
Console.WriteLine(i);
}
sw.Close();
Console.SetOut(orig);
Console.CursorLeft = 0;
Console.WriteLine("100% Complete..");
//Kill the agent to end the task
Console.WriteLine("Killing John to end the program");
John.Die();
Console.WriteLine("John is Dead");
Console.WriteLine("The Simple Hello World Task has finished");
Console.WriteLine("The results have been saved to \"HelloWorldSimple.txt\"");
Console.Write("Press any key to exit");
Console.ReadKey(true);
}
public void Initialize()
{
// Dimension Value Pairs:
sayWhat = World.NewDimensionValuePair("YourAction", "What do you want to do?");
// External Action Chunks:
sayCooperate = World.NewExternalActionChunk("Cooperate");
sayDefect = World.NewExternalActionChunk("Defect");
// placeholder
// GoalChunk salute = World.NewGoalChunk("Salute");
// GoalChunk bidFarewell = World.NewGoalChunk("Bid Farewell");
// WM Actions:
wmuacC = World.NewWorkingMemoryUpdateActionChunk("Remember my opponent cooperated");
wmuacD = World.NewWorkingMemoryUpdateActionChunk("Remember my opponent defected");
DeclarativeChunk dcoc = World.NewDeclarativeChunk("My opponent cooperated");
DeclarativeChunk dcod = World.NewDeclarativeChunk("My opponent defected");
wmuacC.Add(WorkingMemory.RecognizedActions.SET_RESET, dcoc);
wmuacD.Add(WorkingMemory.RecognizedActions.SET_RESET, dcod);
// Set up a two agent model (meaning two agents with the same setup, playing against each other)
Alice = World.NewAgent("Alice");
Bob = World.NewAgent("Bob");
// Simulating environment will determine inputs to each agent based on what each agent does..
// Feedback is determined by payoff matrix..
payoff = new int [2, 2, 2];
// Doing this the hard way. Could set this up all in-line above, but this makes the table
// more explicit in terms of how we want to use it.
// The payoff matrix here is called "Friend or Foe", about the simplest case
// indices mean: FOR-WHICH-AGENT, WHAT-ALICE-DOES, WHAT-BOB-DOES
payoff[_ALICE, _COOPERATE, _COOPERATE] = 1;
payoff[_ALICE, _COOPERATE, _DEFECT] = 0;
payoff[_ALICE, _DEFECT, _COOPERATE] = 2;
payoff[_ALICE, _DEFECT, _DEFECT] = 0;
payoff[_BOB, _COOPERATE, _COOPERATE] = 1;
payoff[_BOB, _COOPERATE, _DEFECT] = 2;
payoff[_BOB, _DEFECT, _COOPERATE] = 0;
payoff[_BOB, _DEFECT, _DEFECT] = 0;
maxpay = 2;
results = new int[_TRIALS, 2, 2];
// Set up a Q-learning Net =
// -- Eligibility Condition = True if "What do you want to do?" is in input, otherwise False
// -- Input = "My opponent cooperated", "My opponent defected", "What do you want to do?"
// -- Output = "I want to defect", "I want to cooperate"
//
// Also, RER is turned ON
QBPNetwork net_A = AgentInitializer.InitializeImplicitDecisionNetwork(Alice, QBPNetwork.Factory, QNetEC);
net_A.Input.Add(sayWhat);
net_A.Input.Add(sayCooperate);
net_A.Input.Add(sayDefect);
net_A.Output.Add(sayCooperate);
net_A.Output.Add(sayDefect);
Alice.Commit(net_A);
net_A.Parameters.LEARNING_RATE = 1;
Alice.ACS.Parameters.PERFORM_RER_REFINEMENT = true; // it's true by default anyway
Alice.ACS.Parameters.LEVEL_SELECTION_METHOD = ActionCenteredSubsystem.LevelSelectionMethods.COMBINED;
Alice.ACS.Parameters.LEVEL_SELECTION_OPTION = ActionCenteredSubsystem.LevelSelectionOptions.FIXED;
Alice.ACS.Parameters.FIXED_FR_LEVEL_SELECTION_MEASURE = 1;
Alice.ACS.Parameters.FIXED_BL_LEVEL_SELECTION_MEASURE = 1;
Alice.ACS.Parameters.FIXED_RER_LEVEL_SELECTION_MEASURE = 1;
Alice.ACS.Parameters.WM_UPDATE_ACTION_PROBABILITY = 1;
// Rules (2 rules) =
// Rule 1:
// -- Condition = "Your opponent cooperated"
// -- Action = Set "My opponent cooperated" in WM
// Rule 2:
// -- Condition = "Your opponent defected"
// -- Action = Set "My opponent defect" in WM
FixedRule ruleA1 = AgentInitializer.InitializeActionRule(Alice, FixedRule.Factory, wmuacC, FRSC);
FixedRule ruleA2 = AgentInitializer.InitializeActionRule(Alice, FixedRule.Factory, wmuacD, FRSC);
Alice.Commit(ruleA1);
Alice.Commit(ruleA2);
QBPNetwork net_B = AgentInitializer.InitializeImplicitDecisionNetwork(Bob, QBPNetwork.Factory, QNetEC);
net_B.Input.Add(sayWhat);
net_B.Input.Add(sayCooperate);
net_B.Input.Add(sayDefect);
net_B.Output.Add(sayCooperate);
net_B.Output.Add(sayDefect);
Bob.Commit(net_B);
// Use Weighted Combination
// NO partial match on TL
net_B.Parameters.LEARNING_RATE = 1;
Bob.ACS.Parameters.PERFORM_RER_REFINEMENT = true;
Bob.ACS.Parameters.LEVEL_SELECTION_METHOD = ActionCenteredSubsystem.LevelSelectionMethods.COMBINED;
Bob.ACS.Parameters.LEVEL_SELECTION_OPTION = ActionCenteredSubsystem.LevelSelectionOptions.FIXED;
Bob.ACS.Parameters.FIXED_FR_LEVEL_SELECTION_MEASURE = 1;
Bob.ACS.Parameters.FIXED_BL_LEVEL_SELECTION_MEASURE = 1;
Bob.ACS.Parameters.FIXED_RER_LEVEL_SELECTION_MEASURE = 1;
Bob.ACS.Parameters.WM_UPDATE_ACTION_PROBABILITY = 1;
FixedRule ruleB1 = AgentInitializer.InitializeActionRule(Bob, FixedRule.Factory, wmuacC, FRSC);
FixedRule ruleB2 = AgentInitializer.InitializeActionRule(Bob, FixedRule.Factory, wmuacD, FRSC);
Bob.Commit(ruleB1);
Bob.Commit(ruleB2);
// Initially using the same parameters for RER as Full Hello World
RefineableActionRule.GlobalParameters.SPECIALIZATION_THRESHOLD_1 = -.6;
RefineableActionRule.GlobalParameters.GENERALIZATION_THRESHOLD_1 = -.1;
RefineableActionRule.GlobalParameters.INFORMATION_GAIN_OPTION = RefineableActionRule.IGOptions.PERFECT;
/*
* Note -- What should be seems is that when you pass in "Your opponent…",
* the agent should return the "Do Nothing" external action
* (since it performed an internal WM action)..
* However, you can just ignore this either way..
*/
}
public void Run()
{
foreach (Tasks t in Enum.GetValues(typeof(Tasks)))
{
int max_i = ((t == Tasks.PERSON) ? 2 * numTestTrials : numTestTrials);
foreach (Groups g in Enum.GetValues(typeof(Groups)))
{
Console.WriteLine("Running Group " + g + " through task " + t);
for (int r = 0; r < numRepeats; r++)
{
Console.Write("Participant #" + r + " is performing the task ");
double currentW = rand.Next(12);
double lastP = rand.Next(12);
Initialize(g);
ActivationCollection irlSI = ImplicitComponentInitializer.NewDataSet();
var irlVars = (from a in As
select from b in Bs
select from c in Cs
select
new
{
A = World.GetDimensionValuePair("A", a),
B = World.GetDimensionValuePair("B", b),
C = World.GetDimensionValuePair("C", c)
}).SelectMany(k => k).SelectMany(k => k);
foreach (var k in irlVars)
{
irlSI.Add(k.A, 1);
irlSI.Add(k.B, 1);
irlSI.Add(k.C, 1);
}
DimensionValuePair targetDV = World.GetDimensionValuePair("Target P", target);
GenerateIRLRuleSet(IRL_Rule_Sets.ONE);
SensoryInformation si = null;
SensoryInformation prevSI;
for (int i = 0; i < max_i; i++)
{
int shift = 10 - (int)Math.Round(10 * ((double)i / (double)max_i));
Console.CursorLeft -= shift;
Console.Write(".");
for (int s = 0; s < shift - 1; s++)
{
Console.Write(" ");
}
if ((from a in John.GetInternals(Agent.InternalContainers.ACTION_RULES) where a is IRLRule select a).Count() == 0)
{
GenerateIRLRuleSet(IRL_Rule_Sets.TWO);
}
prevSI = si;
si = World.NewSensoryInformation(John);
foreach (var s in irlSI)
{
si.Add(s);
}
si.Add(targetDV, 1);
si.Add(World.GetActionChunk(currentW), 1);
lastP = FactoryOutput(lastP, currentW);
si.Add(World.GetDimensionValuePair("Current P", lastP), 1);
if (Math.Abs(lastP - target) < double.Epsilon)
{
if ((t != Tasks.PERSON || (t == Tasks.PERSON && i >= numTestTrials)))
{
results[(int)t, (int)g, r]++;
}
if (prevSI != null)
{
John.ReceiveFeedback(prevSI, 1);
}
}
else
{
if (prevSI != null)
{
John.ReceiveFeedback(prevSI, 0);
}
}
John.Perceive(si);
currentW = (double)John.GetChosenExternalAction(si).LabelAsIComparable;
}
Console.WriteLine();
Console.WriteLine("Participant #" + r + " is finished performing the task and hit the target " +
results[(int)t, (int)g, r] + " times out of " + max_i);
Console.WriteLine("At the end of the task, the participant had the following rules: ");
foreach (var ar in John.GetInternals(Agent.InternalContainers.ACTION_RULES))
{
Console.WriteLine(ar);
}
John.Die();
World.Remove(John);
}
}
Console.WriteLine("Tabular results for the " + t + " task:");
Console.WriteLine("Group\tParticipant\tHits");
foreach (Groups g in Enum.GetValues(typeof(Groups)))
{
for (int i = 0; i < numRepeats; i++)
{
Console.WriteLine(g + "\t" + i + "\t" + results[(int)t, (int)g, i]);
}
}
}
}
static void Main(string[] args)
{
//Initialize the task
Console.WriteLine("Initializing the Full Hello World Task");
int CorrectCounter = 0;
int NumberTrials = 20000;
Random rand = new Random();
World.LoggingLevel = TraceLevel.Off;
int progress = 0;
TextWriter orig = Console.Out;
StreamWriter sw = File.CreateText("HelloWorldFull.txt");
DimensionValuePair hi = World.NewDimensionValuePair("Salutation", "Hello");
DimensionValuePair bye = World.NewDimensionValuePair("Salutation", "Goodbye");
ExternalActionChunk sayHi = World.NewExternalActionChunk("Hello");
ExternalActionChunk sayBye = World.NewExternalActionChunk("Goodbye");
GoalChunk salute = World.NewGoalChunk("Salute");
GoalChunk bidFarewell = World.NewGoalChunk("Bid Farewell");
//Initialize the Agent
Agent John = World.NewAgent("John");
SimplifiedQBPNetwork net = AgentInitializer.InitializeImplicitDecisionNetwork(John, SimplifiedQBPNetwork.Factory);
net.Input.Add(salute, "goals");
net.Input.Add(bidFarewell, "goals");
net.Input.Add(hi);
net.Input.Add(bye);
net.Output.Add(sayHi);
net.Output.Add(sayBye);
net.Parameters.LEARNING_RATE = 1;
John.Commit(net);
John.ACS.Parameters.VARIABLE_BL_BETA = .5;
John.ACS.Parameters.VARIABLE_RER_BETA = .5;
John.ACS.Parameters.VARIABLE_IRL_BETA = 0;
John.ACS.Parameters.VARIABLE_FR_BETA = 0;
RefineableActionRule.GlobalParameters.SPECIALIZATION_THRESHOLD_1 = -.6;
RefineableActionRule.GlobalParameters.GENERALIZATION_THRESHOLD_1 = -.1;
RefineableActionRule.GlobalParameters.INFORMATION_GAIN_OPTION = RefineableActionRule.IGOptions.PERFECT;
AffiliationBelongingnessDrive ab = AgentInitializer.InitializeDrive(John, AffiliationBelongingnessDrive.Factory, rand.NextDouble(), (DeficitChangeProcessor)HelloWorldFull_DeficitChange);
DriveEquation abd = AgentInitializer.InitializeDriveComponent(ab, DriveEquation.Factory);
ab.Commit(abd);
John.Commit(ab);
AutonomyDrive aut = AgentInitializer.InitializeDrive(John, AutonomyDrive.Factory, rand.NextDouble(), (DeficitChangeProcessor)HelloWorldFull_DeficitChange);
DriveEquation autd =
AgentInitializer.InitializeDriveComponent(aut, DriveEquation.Factory);
aut.Commit(autd);
John.Commit(aut);
GoalSelectionModule gsm =
AgentInitializer.InitializeMetaCognitiveModule(John, GoalSelectionModule.Factory);
GoalSelectionEquation gse =
AgentInitializer.InitializeMetaCognitiveDecisionNetwork(gsm, GoalSelectionEquation.Factory);
gse.Input.Add(ab.GetDriveStrength());
gse.Input.Add(aut.GetDriveStrength());
GoalStructureUpdateActionChunk su = World.NewGoalStructureUpdateActionChunk();
GoalStructureUpdateActionChunk bu = World.NewGoalStructureUpdateActionChunk();
su.Add(GoalStructure.RecognizedActions.SET_RESET, salute);
bu.Add(GoalStructure.RecognizedActions.SET_RESET, bidFarewell);
gse.Output.Add(su);
gse.Output.Add(bu);
gsm.SetRelevance(su, ab, 1);
gsm.SetRelevance(bu, aut, 1);
gsm.Commit(gse);
John.Commit(gsm);
John.MS.Parameters.CURRENT_GOAL_ACTIVATION_OPTION =
MotivationalSubsystem.CurrentGoalActivationOptions.FULL;
//Run the task
Console.WriteLine("Running the Full Hello World Task");
Console.SetOut(sw);
SensoryInformation si;
ExternalActionChunk chosen;
for (int i = 0; i < NumberTrials; i++)
{
si = World.NewSensoryInformation(John);
si[AffiliationBelongingnessDrive.MetaInfoReservations.STIMULUS, typeof(AffiliationBelongingnessDrive).Name] = 1;
si[AutonomyDrive.MetaInfoReservations.STIMULUS, typeof(AutonomyDrive).Name] = 1;
//Randomly choose an input to perceive.
if (rand.NextDouble() < .5)
{
//Say "Hello"
si.Add(hi, John.Parameters.MAX_ACTIVATION);
si.Add(bye, John.Parameters.MIN_ACTIVATION);
}
else
{
//Say "Goodbye"
si.Add(hi, John.Parameters.MIN_ACTIVATION);
si.Add(bye, John.Parameters.MAX_ACTIVATION);
}
//Perceive the sensory information
John.Perceive(si);
//Choose an action
chosen = John.GetChosenExternalAction(si);
//Deliver appropriate feedback to the agent
if (chosen == sayHi)
{
//The agent said "Hello".
if (si[hi] == John.Parameters.MAX_ACTIVATION)
{
//The agent responded correctly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was correct");
//Record the agent's success.
CorrectCounter++;
//Give positive feedback.
John.ReceiveFeedback(si, 1.0);
}
else
{
//The agent responded incorrectly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was incorrect");
//Give negative feedback.
John.ReceiveFeedback(si, 0.0);
}
}
else
{
//The agent said "Goodbye".
if (si[bye] == John.Parameters.MAX_ACTIVATION)
{
//The agent responded correctly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was correct");
//Record the agent's success.
CorrectCounter++;
//Give positive feedback.
John.ReceiveFeedback(si, 1.0);
}
else
{
//The agent responded incorrectly
Trace.WriteLineIf(World.LoggingSwitch.TraceWarning, "John was incorrect");
//Give negative feedback.
John.ReceiveFeedback(si, 0.0);
}
}
Console.SetOut(orig);
progress = (int)(((double)(i + 1) / (double)NumberTrials) * 100);
Console.CursorLeft = 0;
Console.Write(progress + "% Complete..");
Console.SetOut(sw);
}
//Report Results
Console.WriteLine("Reporting Results for the Full Hello World Task");
Console.WriteLine("John got " + CorrectCounter + " correct out of " + NumberTrials + " trials (" +
(int)Math.Round(((double)CorrectCounter / (double)NumberTrials) * 100) + "%)");
Console.WriteLine("At the end of the task, John had learned the following rules:");
foreach (var i in John.GetInternals(Agent.InternalContainers.ACTION_RULES))
{
Console.WriteLine(i);
}
sw.Close();
Console.SetOut(orig);
Console.CursorLeft = 0;
Console.WriteLine("100% Complete..");
//Kill the agent to end the task
Console.WriteLine("Killing John to end the program");
John.Die();
Console.WriteLine("John is Dead");
Console.WriteLine("The Full Hello World Task has finished");
Console.WriteLine("The results have been saved to \"HelloWorldFull.txt\"");
Console.Write("Press any key to exit");
Console.ReadKey(true);
}
