public Character(string name, int birthdate, Dynasty dynasty, int money, Game game, Gender gender)
{
Name = name;
BirthDate = birthdate;
Dynasty = dynasty;
Money = money;
WillPower = Game.MAX_WILLPOWER;
Game = game;
Gender = gender;
KnownInformation = new List<InformationInstance>();
traits = new Dictionary<string, Trait>();
jobs = new Dictionary<string, Job>();
prestigeModifiers = new HashSet<PrestigeModifier>();
Children = new List<Character>();
//TODO: have actual personality in the weights.
//TODO: public and private weights should be different
publicWeights = new Weights(this);
privateWeights = new Weights(this);
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
//TODO: Start with random index.
// TODO: Should we be doing some average here instead of just stopping
// at the first one that works?
for (int i = 0; i < context.CurrentCharacter.Children.Count; ++i)
{
Character character = context.CurrentCharacter.Children[i];
context.PushScope(character);
if (requirements.Evaluate(context, game))
{
double result = operation.Evaluate(game, context, weights);
//AnyChild stops after a single interation.
context.PopScope();
return result;
}
context.PopScope();
}
return 0.0;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
double result = 0.0;
for (int i = 0; i < instructions.Length; ++i)
{
result += instructions[i].Evaluate(game, context, weights);
}
return result;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
double result = 0.0;
for (int i = 0; i < outcomes.Length; ++i)
{
result += outcomes[i].Evaluate(game, context, weights) * chances[i];
}
return result;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
return weights.MeasurePrestige(context.CurrentCharacter, change);
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
return weights.MeasureOpinion(context.CurrentCharacter, context.GetScopedObjectByName(character) as Character, game.GetOpinionModifier(identifier));
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
Dictionary<string, object> computedParameters = new Dictionary<string, object>();
foreach (var pair in parameters)
{
computedParameters.Add(pair.Key, context.GetScopedObjectByName(pair.Value));
}
EventContext newContext = new EventContext(context.CurrentCharacter, computedParameters);
return game.GetEventById(eventid).Evaluate(game, newContext, weights);
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
return weights.MeasureGold(context.CurrentCharacter, -gold);
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
if (requirements.Evaluate(context, game))
{
return thenExecute.Evaluate(game, context, weights);
}
else
{
return elseExecute.Evaluate(game, context, weights);
}
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
return weights.MeasureJob(context.CurrentCharacter, game.GetJobById(jobId));
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
double result = 0.0;
foreach (var character in context.CurrentCharacter.CurrentRoom.GetCharacters(context.CurrentCharacter))
{
context.PushScope(character);
if (requirements.Evaluate(context, game))
{
result += operation.Evaluate(game, context, weights);
}
context.PopScope();
}
return result;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
return weights.MeasureAllowEventSelection(context.CurrentCharacter);
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
//The AI needs to understand that it'll get to go again.
return 0.0;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
//Get the weights for the character that will be choosing the character.
Weights charWeights = context.CurrentCharacter.GetWeights(weights.Perspective);
Character[] availableCharacters = game.FilterCharacters(requirements, context).ToArray();
bool[] allowed = new bool[availableCharacters.Length];
if (context.CurrentCharacter is AICharacter)
{
//AICharacter will only select characters from his important character's list.
var important = (context.CurrentCharacter as AICharacter).GetImportantCharacters().Select(pair => pair.Key).ToList();
for (int i = 0; i < availableCharacters.Length; ++i)
{
allowed[i] = important.Contains(availableCharacters[i]);
}
}
else
{
//The player could select anything. We don't know anything about his preferences.
for (int i = 0; i < availableCharacters.Length; ++i)
{
allowed[i] = true;
}
}
//Figure out which ones we think he will choose.
int[] bestIndices = AIHelper.GetBest(availableCharacters, allowed, charWeights, (character, localWeights) =>
{
//We are only considering things theoretically: Don't make any changes to the context
//we are given. We want a new local context for each character so variable changes for
//one character don't influence the others.
EventContext localContext = new EventContext(context);
localContext.PushScope(character, scopeName);
double directResult = operation.Evaluate(game, localContext, localWeights);
//We need to take into account any prestige modifiers because we are throwing away
//the local context now.
return directResult + localWeights.MeasureAfter(localContext, game);
});
//Evaluate each of those character using our weights
double result = 0.0;
foreach(var bestIndex in bestIndices)
{
Character best = availableCharacters[bestIndex];
//We are only considering things theoretically: Don't make any changes to the context
//we are given. We want a new local context for each character so variable changes for
//one character don't influence the others.
EventContext localContext = new EventContext(context);
localContext.PushScope(best, scopeName);
result += operation.Evaluate(game, localContext, weights);
//We need to take into account any prestige modifiers because we are throwing away
//the local context now.
result += weights.MeasureAfter(localContext, game);
}
return result / bestIndices.Length;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
context.PushScope(context.GetScopedObjectByName(scopeName));
double result = operation.Evaluate(game, context, weights);
context.PopScope();
return result;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
if (XmlHelper.IsSpecialName(varName))
throw new InvalidOperationException("Cannot assign to special properties: " + varName);
context.SetVariable(context.CurrentCharacter, varName, newValue.Calculate(context, game));
return 0.0;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
return weights.MeasureObserveChance(context.CurrentCharacter, multiplier);
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
// TODO: We need some way to be intelligent in ChooseInformationAbout for AI character before implementing this.
return 0.0;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
return 0.0;
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
Dictionary<string, object> computedParameters = new Dictionary<string, object>();
foreach (var pair in parameters)
{
computedParameters.Add(pair.Key, context.GetScopedObjectByName(pair.Value));
}
InformationInstance info = new InformationInstance(game.GetInformationById(informationId), computedParameters, game.CurrentTime);
return weights.MeasureObserveInformation(info, game, context.CurrentCharacter);
}
public double Evaluate(Game game, EventContext context, Weights weights)
{
double result = 0.0;
//DirectExecute always happens if it is present.
if (DirectExecute != null)
result += DirectExecute.Evaluate(game, context, weights);
EventOption[] options = GetAvailableOptions(context, game);
if(options.Length == 1)
{
//This is an optimization. We don't need to evaluate another character's perspective
//if they only have a single option.
//We are only considering things theoretically: Don't make any changes to the context
//we are given. We want a new local context for each option so variable changes for
//one option don't influence the others.
EventContext localContext = new EventContext(context);
result += options[0].DirectExecute.Evaluate(game, localContext, weights);
//We need to take into account any prestige modifiers because we are throwing away
//the local context now.
result += weights.MeasureAfter(localContext, game);
}
else if (options.Length > 0)
{
//Get the weights that will be used to decide the best option.
Weights optionWeights = context.CurrentCharacter.GetWeights(weights.Perspective);
//Evaluate each option from the perpsective of the character choosing.
EventOption[] bestOptions = AIHelper.GetBest(options, optionWeights, (option, w) =>
{
//We are only considering things theoretically: Don't make any changes to the context
//we are given. We want a new local context for each option so variable changes for
//one option don't influence the others.
EventContext localContext = new EventContext(context);
double localResult = option.DirectExecute.Evaluate(game, localContext, w);
//We need to take into account any prestige modifiers because we are throwing away
//the local context now.
return localResult + w.MeasureAfter(localContext, game);
});
//The current character will choose one of the best (or so we think)
//We need to evaluate the options from our perspective and average those values.
foreach(var best in bestOptions)
{
//We are only considering things theoretically: Don't make any changes to the context
//we are given. We want a new local context for each option so variable changes for
//one option don't influence the others.
EventContext localContext = new EventContext(context);
result += best.DirectExecute.Evaluate(game, localContext, weights);
//We need to take into account any prestige modifiers because we are throwing away
//the local context now.
result += weights.MeasureAfter(localContext, game);
}
result /= bestOptions.Length;
}
return result;
}
