/// <summary>
/// A* forward search for a plan that satisfies the given goal.
/// </summary>
/// <returns>Returns null if a plan could not be found, or a list of the
/// actions that must be performed, in order.</returns>
public static Queue <ITransition> Plan(
GoapAgent agent,
WorldGoal goal)
{
var worldState = WorldState.Borrow();
worldState[agent] = agent.GetState();
var regressiveSearchGoal = RegressiveSearchWorldGoal.Borrow(goal);
DebugUtils.Assert(worldState[agent].ContainsKey("x") &&
worldState[agent].ContainsKey("y") &&
worldState[agent].ContainsKey("z"),
"Agent's state must contain his position as 'x' and 'y' keys");
var path = AStarSearch.Search(agent, regressiveSearchGoal, worldState, true);
worldState.ReturnSelf();
GoapAction.WithContext.ReportLeaks();
State.ReportLeaks();
WorldState.ReportLeaks();
WorldGoal.ReportLeaks();
RegressiveSearchWorldGoal.ReportLeaks();
WorldEffects.ReportLeaks();
return(path);
}
private float RegressiveSearchCost(RegressiveSearchWorldGoal goal)
{
DebugUtils.Assert(goal != null, "CalculateCost used with something other than WorldState or RegressiveSearchWorldGoal");
// Calculate travel cost.
var travelCost = 0f;
if (actionData.RequiresInRange())
{
if (goal.agentGoalPosition != null)
{
var goalPosition = (Vector3)goal.agentGoalPosition;
var obj = target as Component;
var travelVector = obj.transform.position - goalPosition;
travelCost = travelVector.magnitude;
//DebugUtils.Log(travelCost + " to " + obj.name);
}
}
return(actionData.cost + travelCost);
}
private RegressiveSearchWorldGoal ApplyInReverse(RegressiveSearchWorldGoal goal, bool inPlace = false)
{
var effects = actionData.GetDependentEffects(agent, target);
goal = goal.RegressWorldGoal(effects, inPlace);
effects.ReturnSelf();
goal.AddPreconditions(actionData.GetIndependentPreconditions(agent));
goal.AddPreconditions(actionData.GetDependentPreconditions(agent, target));
// Apply movement.
if (actionData.RequiresInRange())
{
// Don't add the position as a goal because we don't have a
// just MoveAction to satisfy this goal. Instead, we calculate
// this cost as part of the heuristic.
var obj = target as Component;
goal.agentGoalPosition = obj.transform.position;
}
return(goal);
}
/// <summary>
/// Regress worldGoal to before effects
/// </summary>
public RegressiveSearchWorldGoal RegressWorldGoal(WorldEffects worldEffects, bool inPlace = false)
{
var worldGoal = this;
if (!inPlace)
{
worldGoal = RegressiveSearchWorldGoal.Borrow();
foreach (var goal in this)
{
worldGoal[goal.Key] = goal.Value;
}
}
// Reverse change.
foreach (var effects in worldEffects)
{
if (worldGoal.ContainsKey(effects.Key))
{
worldGoal[effects.Key] = worldGoal[effects.Key].RegressGoal(effects.Value);
}
}
return(worldGoal);
}
private float RegressiveSearchCost(RegressiveSearchWorldGoal goal)
{
DebugUtils.Assert(goal != null, "CalculateCost used with something other than WorldState or RegressiveSearchWorldGoal");
// Calculate travel cost.
var travelCost = 0f;
if (actionData.RequiresInRange())
{
if (goal.agentGoalPosition != null)
{
var goalPosition = (Vector2)goal.agentGoalPosition;
var obj = target as Component;
if (obj == null || obj.ToString() == "Null" || obj.ToString() == "null")
{
Debug.LogError("obj is NULL (RegressiveSearchCost)");
return(Mathf.Infinity);
}
var travelVector = (Vector2)obj.transform.position - goalPosition;
travelCost = travelVector.magnitude;
//DebugUtils.Log(travelCost + " to " + obj.name);
}
}
return(actionData.cost + travelCost);
}
