static CardAIPlanData CastSkillOnece(CardAI ai, NetSkill ns, NetCard nc, CardAIPlanData data, NetBattlefield bf, IEnumerator <List <int> > targetEnumerator)
{
//structure makes a new copy
CardAIPlanData result = data;
//using previous bf allows to utilize already cached data, as well as share caching with following casts.
//later we will use new bf to ensure we do not override the same bf.
NetBattlefield prevBf = data.bf;
IList <int> targets = GetPlayCardTargets(ai.FriendlyTurn, ns, nc, prevBf);
if (targets == null || targets.Count < 1)
{
return(new CardAIPlanData());
}
if (targetEnumerator == null)
{
targetEnumerator = GetTargetEnumerator(ns, nc, targets, ai);
if (targetEnumerator == null)
{
return(new CardAIPlanData());
}
}
targetEnumerator.MoveNext();
List <int> selectedTargets = targetEnumerator.Current;
if (selectedTargets == null || selectedTargets.Count == 0)
{
return(new CardAIPlanData());
}
List <int> secTargets = GetSecondaryPlayTargets(ns, nc, prevBf, selectedTargets, ai.r);
FInt skillDelay = ns.GetSkillDelay(prevBf);
int cost = prevBf.GetCardCastingCostFast(ns.GetOwner(prevBf).CardID);
//add cost if something need to be casted next turn by AI
var ncp = bf.GetPlayerByPlayerID(ai.playerID);
if (cost > ncp.ApLeft)
{
skillDelay += 2;
}
//operate from now on its own bf
result.bf = bf;
NetQueueItem q;
if (ns.IsCastingSpell())
{
q = new NetQueueItem(bf, ns, new NetListInt(selectedTargets), new NetListInt(secTargets), skillDelay, -1);
}
else
{
q = new NetQueueItem(bf, ns, null, null, skillDelay, selectedTargets[0]);
}
//if ap cost were larger than current ap then we will result in negative ap.
//because its just simulation we do not care for that now, as the sum of this and next turn ap
//for estimating cost would be identical
ncp.ApLeft -= nc.GetCastingCost();
bf.PlayCard(q, ai.r);
float value = bf.GetValueByCloneSimulation(ai.iterations, ai.r);
result.value = value;
result.AddCardPlay(nc.CardID, ns.SkillInBattleID, q);
result.SetEnumerator(targetEnumerator);
result.valid = true;
return(result);
}
// AI 2.0
#region Core planning
static public CardAIPlanData ProducePlan(CardAI ai, CardAIPlanData data, bool allowTwoStages)
{
if (ai.intensity < 1)
{
Debug.LogError("[ERROR]0 level for AI will not produce any plans!, Increase calculation intensity to minimum 1!");
return(new CardAIPlanData());
}
NetBattlefield bf = data.bf;
int apNextTurn = bf.APNextTurn(ai.playerID);
int leftAP = bf.LeftAPThisTurn(ai.playerID) + apNextTurn;
float value = bf.GetValueByCloneSimulation(ai.iterations, ai.r);
List <NetCard> cards = CardsWithinAPRange(ai, bf, ai.totalAP);
if (cards == null)
{
return(new CardAIPlanData());
}
List <NetSkill> supportSkills2 = new List <NetSkill>();
List <NetSkill> skills = SelectSkillsToCast(cards);
if (skills == null || skills.Count == 0)
{
Debug.LogWarning("[!! AI] Avaliable skills to cast = 0");
return(new CardAIPlanData());
}
List <NetSkill> skills2 = null;
List <int> importantPos = null;
#region detect potential two-cast suggestions
if (allowTwoStages)
{
List <NetCard> cards2 = CardsWithinAPRange(ai, bf, ai.totalAP - 1);
if (cards2 != null)
{
skills2 = SelectSkillsToCast(cards2);
//two cast simulation would be considered only in case of at least two skills
//being of value for that activity
if (skills2 != null && skills2.Count > 1)
{
supportSkills2 = WillDoTwoCastSimulation(ai, data, skills2);
if (supportSkills2.Count > 0)
{
importantPos = FindImportantPositions(ai, data, supportSkills2);
}
//expensive casts which exhausts available AP will be casted as single-casts
//support skills will be casted as single-casts
//cheap enough non-supports will be BASE for multi-casts
//then supports skills will be casted on top
skills2 = skills2.Except(supportSkills2).ToList();
skills = skills.Except(skills2).ToList();
}
}
}
#endregion
#region produce single-cast results
List <CardAIPlanData> plans = ProduceCastPlans(ai, data, skills, 1);
if (plans != null && plans.Count > 0)
{
plans.Sort(PlanSorter);
plans = ProduceRefinePlans(ai, data, plans);
}
#endregion
#region produce two-cast results.
//TODO start by doing first layer of casts, with preferences if possible
if (skills2 != null && skills2.Count > 0)
{
List <CardAIPlanData> plans2 = ProduceCastPlans(ai, data, skills2, 1);
if (plans2 != null && plans2.Count > 0)
{
plans2.Sort(PlanSorter);
plans2 = ProduceRefinePlans(ai, data, plans2);
plans.AddRange(plans2);
}
//if supports are possible validate do another single turn pass for remaining skills.
if (supportSkills2 != null && supportSkills2.Count > 0)
{
//register important positions if any to ensure they are preferred during position planning.
ai.preferredStrategicPlaces = importantPos;
//First Cast
plans2 = ProduceCastPlans(ai, data, skills2, 1);
if (plans2 != null && plans2.Count > 0)
{
plans2.Sort(PlanSorter);
plans2 = ProduceRefinePlans(ai, data, plans2);
//Second Cast (aka support cast)
//Use first plan and select boosts which seems to be most efficient among available options.
//this does one base cast and then one boost for each which account for: AxB
List <CardAIPlanData> supportPlans = new List <CardAIPlanData>();
if (plans2 != null && plans2.Count > 0)
{
for (int i = 0; i < ai.supportCasts && i < plans2.Count; i++)
{
CardAIPlanData plan = plans2[i];
supportPlans.AddRange(ProduceCastPlans(ai, plan, supportSkills2, 1));
}
}
// select the best result of AxB to find the best theoretical result after two casts
//do deeper planning for the base positioning and then cast support skills on the chosen plan to
//acquire actual final value
supportPlans.Sort(PlanSorter);
if (supportPlans.Count > 0)
{
CardAIPlanData cpd = supportPlans[0];
CardAIPlanData planBase = plans2.Find(o => o.firstPlay.skill == cpd.firstPlay.skill);
CardAIPlanData result = ProduceSingleRefination(ai, data, planBase);
if (result.valid)
{
List <CardAIPlanData> results = ProduceCastPlans(ai, result, supportSkills2, 1);
if (results != null && results.Count > 0)
{
results.Sort(PlanSorter);
results = ProduceRefinePlans(ai, data, results);
plans.AddRange(results);
}
}
}
}
}
}
#endregion
#region compare all results and select the best
// all results are compared based on how much AP they use.
// positive gain produced by all results are based on final result and final AP cost
// GainedAdvantage / (2 + AP used) which results in:
// 1/2 multiplier for 1 AP actions
// 1/3 multiplier for 2 AP actions
// 1/4 multiplier for 3+ AP actions
// Single-casts would be considered as if they would use-up AvaliableAP amount
// to ensure we do not consider them better even though they cannot provide any better results
//if two-cast actions are available to some actions
// aka: there is no point in having cheaper cost if AP cannot be later used.
if (plans.Count == 0)
{
Debug.LogWarning("[!! AI] Available plans = 0");
return(new CardAIPlanData());
}
CardAIPlanData selected = plans[0];
float selectedV = float.MinValue;
for (int i = 0; i < plans.Count; i++)
{
CardAIPlanData cpd = plans[i];
int newLeftAP = cpd.bf.LeftAPThisTurn(ai.playerID) + apNextTurn;
int apUsed = leftAP - newLeftAP;
float v = cpd.value - value;
v = v / (1f + apUsed);
if (selectedV < v)
{
selected = plans[i];
selectedV = v;
}
}
#endregion
return(selected);
}