/// <summary>
/// Traversal of <see cref="DecisionTree"/> is done by recusrively calling the <see cref="ADecisionNode.TreeTraversal(ADecisionNode)"/>.
/// </summary>
public static RefList <AIDecisionTreeChoice> traverseDecisionTree(DecisionTree p_decisionTree)
{
RefList <AIDecisionTreeChoice> l_choices = new RefList <AIDecisionTreeChoice>();
RefList <TraversalStack> l_traversalStacks = new RefList <TraversalStack>();
l_traversalStacks.Add(TraversalStack.build(p_decisionTree.RootNode));
while (l_traversalStacks.Count > 0)
{
ref TraversalStack l_currentTraversalStack = ref l_traversalStacks.ValueRef(l_traversalStacks.Count - 1);
//If there if the current node has links
if (l_currentTraversalStack.DecisionNode.LinkedNodes != null)
{
if (l_currentTraversalStack.LinkIterationCounter < l_currentTraversalStack.DecisionNode.LinkedNodes.Count)
{
//We traverse the link and go one level deeper
ADecisionNode l_nextNode = l_currentTraversalStack.DecisionNode.LinkedNodes[l_currentTraversalStack.LinkIterationCounter];
l_currentTraversalStack.LinkIterationCounter += 1;
TraversalStack l_oneLevelDepperStack = TraversalStack.build(l_nextNode);
l_traversalStacks.AddRef(ref l_oneLevelDepperStack);
l_nextNode.TreeTraversal(l_currentTraversalStack.DecisionNode);
}
else
{
#region Updating stack
l_traversalStacks.RemoveAt(l_traversalStacks.Count - 1);
#endregion
}
}
else // No links have been found, this means that the current node is a leaf node.
{
#region Creating the choice as the current node is a leaf
ADecisionNode[] l_choiceNodes = new ADecisionNode[l_traversalStacks.Count];
for (int i = 0; i < l_traversalStacks.Count; i++)
{
l_choiceNodes[i] = l_traversalStacks.ValueRef(i).DecisionNode;
}
AIDecisionTreeChoice l_choice = AIDecisionTreeChoice.build(l_choiceNodes);
l_choices.AddRef(ref l_choice);
#endregion
l_traversalStacks.RemoveAt(l_traversalStacks.Count - 1);
}
}
/// <summary>
/// Picking the best choice between <paramref name="p_choices"/>.
/// The choice is made by associating a score to every choices. The choice with the highest score is picked.
/// </summary>
/// <param name="p_choices"> Compared choices. </param>
/// <returns> The picked choice </returns>
public static ref AIDecisionTreeChoice defaultTestPickChoice(RefList <AIDecisionTreeChoice> p_choices, Entity p_calledEntity)
{
// Becuase PathScore represents the distance crossed and that we want to minimize movement,
// we normalize the PathScore by it's potential maxmimum value calculated if the Entity were using all it's ActionPoints to move.
ActionPoint l_calledEntityActionPoint = EntityComponent.get_component <ActionPoint>(p_calledEntity);
float l_maxPathScoreThatCanBeCrossed = _ActionPoint.Calculations.actionPointToCrossableWorldDistance(l_calledEntityActionPoint.ActionPointData.CurrentActionPoints);
RefList <AIDecisionScore> l_choiceScores = new RefList <AIDecisionScore>(p_choices.Count);
for (int i = 0; i < p_choices.Count; i++)
{
AIDecisionScore l_choiceScore = AIDecisionScore.build();
ref AIDecisionTreeChoice l_aIDecisionTreeChoice = ref p_choices.ValueRef(i);
for (int j = 0; j < l_aIDecisionTreeChoice.DecisionNodesChoiceOrdered.Length; j++)
{
switch (l_aIDecisionTreeChoice.DecisionNodesChoiceOrdered[j])
{
case MoveToNavigationNodeNode l_moveToNavigationNodeNode:
{
l_choiceScore.PathScore += math.max(l_maxPathScoreThatCanBeCrossed - l_moveToNavigationNodeNode.PathCost, 0.0f);
break;
}
case AttackNode l_attackNode:
{
l_choiceScore.DamageScore += l_attackNode.DamageDone;
break;
}
case HealNode l_healNode:
{
l_choiceScore.HealScore += l_healNode.RecoveredHealth;
break;
}
}
}
l_choiceScores.AddRef(ref l_choiceScore);
}
