/// <summary>
/// This method initializes the dimension-value pairs and declarative chunks in the <see cref="World"/>
/// </summary>
static void InitializeWorld(Agent a)
{
//Initialize the dimension-value pairs
for (int i = 1; i <= nodeCount; i++)
{
dvs.Add(World.NewDistributedDimensionValuePair(a, i));
}
//Initializes the declarative chunks
for (int i = 0; i < patterns.Length; i++)
{
//Generates a declarative chunk and specifies that the semantic label associated with the declarative chunk should NOT be added to the
//dimension-value pairs of that chunk
DeclarativeChunk dc =
World.NewDeclarativeChunk(i, addSemanticLabel: false);
//Adds the appropriate dimension-value pairs (as indicated by the "patterns" array) for each declarative chunk pattern representation
foreach (var dv in dvs)
{
if (patterns[i].Contains(dv.Value))
{
dc.Add(dv);
}
}
//Adds the declarative chunk to the chunks list
chunks.Add(dc);
}
}
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 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..
*/
}
static void InitializeWorld()
{
string feature = " ";
//Initialize the dimension-value pairs
for (int i = 1; i <= nodeCount; i++)
{
//names for features
if (i == 1)
{
feature = "lactates";
}
else if (i == 2)
{
feature = "hair/fur";
}
else if (i == 3)
{
feature = "warm-blooded";
}
else if (i == 4)
{
feature = "teeth";
}
else if (i == 5)
{
feature = "tails";
}
else if (i == 6)
{
feature = "lives on land";
}
else if (i == 7)
{
feature = "omnivores";
}
else if (i == 8)
{
feature = "herbivores";
}
else if (i == 9)
{
feature = "carnivores";
}
else if (i == 10)
{
feature = "small";
}
else if (i == 11)
{
feature = "large";
}
else if (i == 12)
{
feature = "something";
}
else if (i == 13)
{
feature = "wild";
}
else if (i == 14)
{
feature = "domesticated";
}
else if (i == 15)
{
feature = "swims";
}
else if (i == 16)
{
feature = "horns";
}
dvs.Add(World.NewDimensionValuePair(feature, i));
}
//Initializes the declarative chunks
for (int i = 0; i < patterns.Length; i++)
{
//Generates a declarative chunk
string mammal_name = " ";
if (i == 0)
{
mammal_name = "mammal";
}
else if (i == 1)
{
mammal_name = "hippo";
}
else if (i == 2)
{
mammal_name = "rhino";
}
else if (i == 3)
{
mammal_name = "hamster";
}
DeclarativeChunk dc =
World.NewDeclarativeChunk(mammal_name, addSemanticLabel: false);
//Adds the appropriate dimension-value pairs (as indicated by the "patterns" array) for each declarative chunk pattern representation
foreach (var dv in dvs)
{
if (patterns[i].Contains(dv.Value))
{
dc.Add(dv);
}
}
//Adds the declarative chunk to the chunks list
chunks.Add(dc);
}
}
static void InitializeWorld()
{
string feature = " ";
//Initialize the dimension-value pairs
for (int i = 1; i <= nodeCount; i++)
{
//features for DV pairs
if (i == 1)
{
feature = "wings";
}
else if (i == 2)
{
feature = "beaks";
}
else if (i == 3)
{
feature = "lays eggs";
}
else if (i == 4)
{
feature = "bird";
}
else if (i == 5)
{
feature = "flies south for winter";
}
else if (i == 6)
{
feature = "eats berries";
}
else if (i == 7)
{
feature = "eats plants (grass)";
}
else if (i == 8)
{
feature = "swims";
}
else if (i == 9)
{
feature = "builds nests";
}
else if (i == 10)
{
feature = "small";
}
else if (i == 11)
{
feature = "big";
}
else if (i == 12)
{
feature = "red";
}
else if (i == 13)
{
feature = "blue";
}
else if (i == 14)
{
feature = "yellow";
}
else if (i == 15)
{
feature = "brownish";
}
dvs.Add(World.NewDimensionValuePair(feature, i));
}
//Initializes the declarative chunks
for (int i = 0; i < patterns.Length; i++)
{
//Generates a declarative chunk
string bird_name = " ";
if (i == 0)
{
bird_name = "robin";
}
else if (i == 1)
{
bird_name = "sparrow";
}
else if (i == 2)
{
bird_name = "goose";
}
DeclarativeChunk dc =
World.NewDeclarativeChunk(bird_name, addSemanticLabel: false);
//Adds the appropriate dimension-value pairs (as indicated by the "patterns" array) for each declarative chunk pattern representation
foreach (var dv in dvs)
{
if (patterns[i].Contains(dv.Value))
{
dc.Add(dv);
}
}
//Adds the declarative chunk to the chunks list
chunks.Add(dc);
}
}
static void InitializeWorld()
{
string feature = " ";
//Initializes the dimension-value pairs
for (int i = 1; i <= nodeCount; i++)
{
if (i == 1)
{
feature = "wings";
}
else if (i == 2)
{
feature = "flippers";
}
else if (i == 3)
{
feature = "beaks";
}
else if (i == 4)
{
feature = "lays eggs";
}
else if (i == 5)
{
feature = "migrates in winter";
}
else if (i == 6)
{
feature = "eats bugs and berries";
}
else if (i == 7)
{
feature = "eats fish";
}
else if (i == 8)
{
feature = "builds nests";
}
else if (i == 9)
{
feature = "flies";
}
else if (i == 10)
{
feature = "swims";
}
else if (i == 11)
{
feature = "feathers";
}
else if (i == 12)
{
feature = "colorful";
}
else if (i == 13)
{
feature = "lives in trees";
}
else if (i == 14)
{
feature = "lives in the polar regions";
}
dvs.Add(World.NewDimensionValuePair(feature, i));
}
//Initializes the declarative chunks
for (int i = 0; i < patterns.Length; i++)
{
//Generates a declarative chunk
string bird_name = " ";
if (i == 0)
{
bird_name = "birds";
}
else if (i == 1)
{
bird_name = "robins";
}
else if (i == 2)
{
bird_name = "penguins";
}
DeclarativeChunk dc =
World.NewDeclarativeChunk(bird_name, addSemanticLabel: false);
//Adds the appropriate dimension-value pairs (as indicated by the "patterns" array) for each declarative chunk pattern representation
foreach (var dv in dvs)
{
if (patterns[i].Contains(dv.Value))
{
dc.Add(dv);
}
}
//Adds the declarative chunk to the chunks list
chunks.Add(dc);
}
}
