def getAlphaBetaMove(board, parser):
import MoveNode as MoveNode
depth = 1
AIagent = AI(board, True, depth)
legalMoves = board.getAllMovesLegal(board.currentSide)
bestMove = None
moveTree = AIagent.generateMoveTree()
legalBestMoves = AIagent.bestMovesWithMoveTree(moveTree)
#conLegalMoves = AIagent.getAllMovesLegalConcurrent(board.currentSide)
def alphaBetaMax(alpha, beta, depth):
global bestMove
value = -float(999999)
#if (depth == 0 or board.isCheckmate or board.isStalemate):
if (depth == 0):
return (board.getPointValueOfSide(board.currentSide), )
random.shuffle(legalBestMoves)
for move in legalBestMoves:
random.shuffle(legalMoves)
for m in legalMoves:
random.shuffle(legalMoves)
value = max(value, alphaBetaMin(alpha, beta, depth - 1)[0])
if (value >= beta):
return beta, m
if (value > alpha):
alpha = value
bestMove = m
bestMove.notation = parser.notationForMove(bestMove)
#return {'alpha': alpha, 'bestMove': bestMove}
return alpha, bestMove
#return bestMove
def alphaBetaMin(alpha, beta, depth):
global bestMove
value = float(999999)
#if (depth == 0 or board.isCheckmate or board.isStalemate):
if (depth == 0):
return (-board.getPointValueOfSide(board.currentSide), )
random.shuffle(legalBestMoves)
for move in legalBestMoves:
random.shuffle(legalMoves)
for m in legalMoves:
random.shuffle(legalMoves)
value = min(value, alphaBetaMax(alpha, beta, depth - 1)[0])
if (value <= alpha):
return alpha, m
if (value < beta):
beta = value
bestMove = m
bestMove.notation = parser.notationForMove(bestMove)
#return {'beta': beta, 'bestMove': bestMove}
return beta, bestMove
#return bestMove
score, action = alphaBetaMax(-999999, 999999, 3)
return score, action
