/// <summary>
/// Executes social learning process of current agent for specific rule set layer
/// </summary>
/// <param name="agent"></param>
/// <param name="lastIteration"></param>
/// <param name="layer"></param>
public void ExecuteLearning(IAgent agent, LinkedListNode <Dictionary <IAgent, AgentState> > lastIteration, RuleLayer layer)
{
Dictionary <IAgent, AgentState> priorIterationState = lastIteration.Previous.Value;
agent.ConnectedAgents.Randomize().ForEach(neighbour =>
{
IEnumerable <Rule> activatedRules = priorIterationState[neighbour].RuleHistories
.SelectMany(rh => rh.Value.Activated).Where(r => r.Layer == layer);
activatedRules.ForEach(rule =>
{
if (agent.AssignedRules.Contains(rule) == false)
{
agent.AssignNewRule(rule, neighbour.AnticipationInfluence[rule]);
}
});
});
}
/// <summary>
/// Executes agent innovation process for specific site
/// </summary>
/// <param name="agent"></param>
/// <param name="lastIteration"></param>
/// <param name="goal"></param>
/// <param name="layer"></param>
public void Execute(IAgent agent, LinkedListNode <Dictionary <IAgent, AgentState> > lastIteration, Goal goal,
RuleLayer layer, ActiveSite site)
{
Dictionary <IAgent, AgentState> currentIteration = lastIteration.Value;
Dictionary <IAgent, AgentState> priorIteration = lastIteration.Previous.Value;
//gets prior period activated rule
RuleHistory history = priorIteration[agent].RuleHistories[site];
Rule priorPeriodRule = history.Activated.Single(r => r.Layer == layer);
LinkedListNode <Dictionary <IAgent, AgentState> > tempNode = lastIteration.Previous;
//if prior period rule is do nothing rule then looking for any do something rule
while (priorPeriodRule.IsAction == false && tempNode.Previous != null)
{
tempNode = tempNode.Previous;
history = tempNode.Value[agent].RuleHistories[site];
priorPeriodRule = history.Activated.Single(r => r.Layer == layer);
}
//if the layer or prior period rule are modifiable then generate new rule
if (layer.LayerConfiguration.Modifiable || (!layer.LayerConfiguration.Modifiable && priorPeriodRule.IsModifiable))
{
RuleLayerConfiguration parameters = layer.LayerConfiguration;
Goal selectedGoal = goal;
GoalState selectedGoalState = lastIteration.Value[agent].GoalsState[selectedGoal];
#region Generating consequent
double min = parameters.MinValue(agent);
double max = parameters.MaxValue(agent);
double consequentValue = priorPeriodRule.Consequent.Value;
switch (selectedGoalState.AnticipatedDirection)
{
case AnticipatedDirection.Up:
{
if (RuleLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Positive)
{
max = Math.Abs(consequentValue - max);
consequentValue += (Math.Abs(PowerLawRandom.GetInstance.Next(min, max) - max));
}
if (RuleLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Negative)
{
max = Math.Abs(consequentValue - min);
consequentValue -= (Math.Abs(PowerLawRandom.GetInstance.Next(min, max) - max));
}
break;
}
case AnticipatedDirection.Down:
{
if (RuleLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Positive)
{
max = Math.Abs(consequentValue - min);
consequentValue -= (Math.Abs(PowerLawRandom.GetInstance.Next(min, max) - max));
}
if (RuleLayerConfiguration.ConvertSign(parameters.ConsequentRelationshipSign[goal.Name]) == ConsequentRelationship.Negative)
{
max = Math.Abs(consequentValue - max);
consequentValue += (Math.Abs(PowerLawRandom.GetInstance.Next(min, max) - max));
}
break;
}
default:
{
throw new Exception("Not implemented for AnticipatedDirection == 'stay'");
}
}
RuleConsequent consequent = RuleConsequent.Renew(priorPeriodRule.Consequent, consequentValue);
#endregion
#region Generating antecedent
List <RuleAntecedentPart> antecedentList = new List <RuleAntecedentPart>(priorPeriodRule.Antecedent.Length);
foreach (RuleAntecedentPart antecedent in priorPeriodRule.Antecedent)
{
dynamic newConst = agent[antecedent.Param];
RuleAntecedentPart newAntecedent = RuleAntecedentPart.Renew(antecedent, newConst);
antecedentList.Add(newAntecedent);
}
#endregion
AgentState agentState = currentIteration[agent];
Rule generatedRule = Rule.Renew(priorPeriodRule, antecedentList.ToArray(), consequent);
//change base ai values for the new rule
double consequentChangeProportion = (generatedRule.Consequent.Value - priorPeriodRule.Consequent.Value) / (double)priorPeriodRule.Consequent.Value;
Dictionary <Goal, double> baseAI = agent.AnticipationInfluence[priorPeriodRule];
Dictionary <Goal, double> proportionalAI = new Dictionary <Goal, double>();
agent.AssignedGoals.ForEach(g =>
{
double ai = baseAI[g];
ConsequentRelationship relationship = RuleLayerConfiguration.ConvertSign(priorPeriodRule.Layer.LayerConfiguration.ConsequentRelationshipSign[g.Name]);
double difference = Math.Abs(ai * consequentChangeProportion);
switch (selectedGoalState.AnticipatedDirection)
{
case AnticipatedDirection.Up:
{
if (relationship == ConsequentRelationship.Positive)
{
ai += difference;
}
else
{
ai -= difference;
}
break;
}
case AnticipatedDirection.Down:
{
if (relationship == ConsequentRelationship.Positive)
{
ai -= difference;
}
else
{
ai += difference;
}
break;
}
}
proportionalAI.Add(g, ai);
});
//add the generated rule to the prototype's mental model and assign one to the agent's mental model
if (agent.Prototype.IsSimilarRuleExists(generatedRule) == false)
{
//add to the prototype and assign to current agent
agent.AddRule(generatedRule, layer, proportionalAI);
}
else if (agent.AssignedRules.Any(rule => rule == generatedRule) == false)
{
//assign to current agent only
agent.AssignNewRule(generatedRule, proportionalAI);
}
if (layer.Set.Layers.Count > 1)
{
//set consequent to actor's variables for next layers
generatedRule.Apply(agent);
}
}
}
