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ChessAI.py
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ChessAI.py
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import random
import numpy as np
# White Winning +ve Value
# Black Winning -ve Value
pieceScore = {
"K": 0,
"Q": 9,
"R": 5,
"B": 3,
"N": 3,
"P": 1
}
CHECKMATE = 1000
STALEMATE = 0
DEPTH = 2
POSITIONAL_WEIGHT = 0.2
#POSITIONAL TABLES RANGE(1-4) NEEEEDDD TO BE TUNED
KTable = np.array ([ #positional table for king
[3 , 3 , 2 , 1 , 1 , 2 , 3 , 3],
[2 , 2 , 1 , 1 , 1 , 1 , 2 , 2],
[1 , 0 , 0 , 0 , 0 , 0 , 0 , 1],
[0 , 0 , 0 , 0 , 0 , 0 , 0 , 0],
[0 , 0 , 0 , 0 , 0 , 0 , 0 , 0],
[1 , 0 , 0 , 0 , 0 , 0 , 0 , 1],
[2 , 2 , 1 , 1 , 1 , 1 , 2 , 2],
[3 , 3 , 2 , 1 , 1 , 2 , 3 , 3],
])
QTable = np.array ([ #positional table for queen
[0 , 1 , 1 , 1 , 1 , 1 , 1 , 0],
[1 , 2 , 2 , 2 , 2 , 2 , 2 , 1],
[1 , 3 , 3 , 3 , 3 , 3 , 2 , 1],
[2 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 2 , 2 , 2 , 2 , 2 , 1],
[0 , 1 , 1 , 1 , 1 , 1 , 1 , 0],
])
RTable = np.array ([ #positional table for rook
[1 , 0 , 1 , 1 , 1 , 1 , 0 , 1],
[1 , 4 , 4 , 4 , 4 , 4 , 4 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 4 , 4 , 4 , 4 , 4 , 4 , 1],
[1 , 0 , 1 , 1 , 1 , 1 , 0 , 1],
])
BTable = np.array ([ #positional table for bishop
[0 , 1 , 1 , 1 , 1 , 1 , 1 , 0],
[1 , 3 , 2 , 2 , 2 , 2 , 3 , 1],
[1 , 4 , 4 , 4 , 4 , 4 , 4 , 1],
[1 , 2 , 3 , 4 , 4 , 3 , 2 , 1],
[1 , 2 , 3 , 4 , 4 , 3 , 2 , 1],
[1 , 2 , 4 , 4 , 4 , 4 , 2 , 1],
[1 , 3 , 2 , 2 , 2 , 2 , 3 , 1],
[0 , 1 , 1 , 1 , 1 , 1 , 1 , 0],
])
NTable = np.array ([ #positional table for knight
[0 , 1 , 1 , 1 , 1 , 1 , 1 , 0],
[1 , 2 , 2 , 2 , 2 , 2 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 3 , 4 , 4 , 3 , 2 , 1],
[1 , 2 , 3 , 4 , 4 , 3 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 2 , 2 , 2 , 2 , 2 , 1],
[0 , 1 , 1 , 1 , 1 , 1 , 1 , 0],
])
PTable = np.array ([ #positional table for pawn
[1 , 1 , 1 , 1 , 1 , 1 , 1 , 1],
[1 , 2 , 2 , 1 , 1 , 2 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 3 , 4 , 4 , 3 , 2 , 1],
[1 , 2 , 3 , 4 , 4 , 3 , 2 , 1],
[1 , 2 , 3 , 3 , 3 , 3 , 2 , 1],
[1 , 2 , 2 , 1 , 1 , 2 , 2 , 1],
[1 , 1 , 1 , 1 , 1 , 1 , 1 , 1],
])
positionalTableMap = {
"K": KTable,
"Q": QTable,
"R": RTable,
"B": BTable,
"N": NTable,
"P": PTable
}
def ScoreMaterial(board):
score = 0
for row in board:
for ele in row:
if ele[0] == "w":
score += pieceScore[ele[1]]
elif ele[0] == "b":
score -= pieceScore[ele[1]]
return score
def ScoreBoard(gameState):
if gameState.checkmate:
if gameState.whiteToMove:
return -CHECKMATE #blackwins
else:
return CHECKMATE #blackwins
elif gameState.stalemate:
return STALEMATE #score 0
score = 0
for row in range(len(gameState.board)):
for col in range(len(gameState.board[0])):
piece = gameState.board[row][col]
if piece !="--":
#transpositional score
positionScore = POSITIONAL_WEIGHT * positionalTableMap[piece[1]][row][col]
if piece[0]=="w":
score+= pieceScore[piece[1]] + positionScore
elif piece[0]=="b":
score-= pieceScore[piece[1]] + positionScore
return score
def RandomAI(validMoves):
return random.choice(validMoves)
def GreedyAI(gameState,validMoves):
turnSign = 1 if gameState.whiteToMove else -1
# IF AI = WHITE THEN 1, IF AI = BLACK THEN -1
bestScore = - CHECKMATE #init the worst possible score
bestMove = None
for aiMove in validMoves:
gameState.makeMove(aiMove)
if gameState.checkmate:
bestScore = turnSign * CHECKMATE
elif gameState.stalemate:
bestScore = STALEMATE
score = turnSign * ScoreBoard(gameState.board)
if (score > bestScore):
bestScore = score
bestMove = aiMove
gameState.undoMove()
return bestMove
def DepthTwoMinMaxAI(gameState,validMoves):
turnSign = 1 if gameState.whiteToMove else -1
MinMaxScore = CHECKMATE # init the worst possible score
bestAIMove = None
random.shuffle(validMoves)
for aiMove in validMoves:
gameState.makeMove(aiMove)
# FIND OPPONENTS MAX SCORE
oppMoves = gameState.getValidMoves()
oppMaxScore = -CHECKMATE
for oppMove in oppMoves:
gameState.makeMove(oppMove)
if gameState.checkmate:
score = CHECKMATE
elif gameState.stalemate:
score = STALEMATE
else:
score = -turnSign * ScoreBoard(gameState.board)
if (score > oppMaxScore):
oppMaxScore = score
gameState.undoMove()
# FIND YOUR MIN SCORE
if oppMaxScore < MinMaxScore:
MinMaxScore = oppMaxScore
bestAIMove = aiMove
gameState.undoMove()
return bestAIMove
def MinMaxAI(gameState,validMoves):
global nextMove
nextMove = None
random.shuffle(validMoves)
RecursiveMinMax(gameState,validMoves,DEPTH,gameState.whiteToMove)
return nextMove
def RecursiveMinMax(gameState,validMoves,depth,whiteToMove):
global nextMove
if depth == 0:
return ScoreMaterial(gameState.board)
if whiteToMove: #MAXIMIZER
maxScore = -CHECKMATE
for move in validMoves:
gameState.makeMove(move)
nextMoves = gameState.getValidMoves()
score = RecursiveMinMax(gameState,nextMoves,depth-1,not whiteToMove)
if score > maxScore:
maxScore = score
if depth == DEPTH:
nextMove = move
gameState.undoMove()
return maxScore
else: #MINIMIZER
minScore = CHECKMATE
for move in validMoves:
gameState.makeMove(move)
nextMoves = gameState.getValidMoves()
score = RecursiveMinMax(gameState,nextMoves,depth-1,not whiteToMove)
if score < minScore:
minScore = score
if depth == DEPTH:
nextMove = move
gameState.undoMove()
return minScore
def NegaMaxAI(gameState,validMoves):
global nextMove, counter
nextMove = None
random.shuffle(validMoves)
# counter =0
RecursiveNegaMax(gameState, validMoves, DEPTH, (1 if gameState.whiteToMove else -1))
# print(counter)
return nextMove
def RecursiveNegaMax(gameState,validMoves,depth,turnMultiplier):
global nextMove, counter
# counter +=1
if depth == 0:
return turnMultiplier * ScoreBoard(gameState)
maxScore = -CHECKMATE # init with the worst possible value
for move in validMoves:
gameState.makeMove(move)
nextMoves = gameState.getValidMoves()
score = -1 * RecursiveNegaMax(gameState, nextMoves, depth - 1,-1*turnMultiplier)
if score > maxScore:
maxScore = score
if depth == DEPTH:
nextMove = move
gameState.undoMove()
return maxScore
def AlphaBetaPruningAI(gameState,validMoves):
global nextMove, counter
nextMove = None
random.shuffle(validMoves)
# counter = 0
RecursiveAlphaBetaPruning(gameState, validMoves,DEPTH,-CHECKMATE,CHECKMATE, (1 if gameState.whiteToMove else -1))
# print(counter)
return nextMove
def RecursiveAlphaBetaPruning(gameState,validMoves,depth,alpha,beta,turnMultiplier):
#alpha is max rn and beta is min score rn
global nextMove, counter
# counter +=1
if depth == 0:
return turnMultiplier * ScoreBoard(gameState)
# order moves - implement later for better efficiency
maxScore = -CHECKMATE # init with the worst possible value
for move in validMoves:
gameState.makeMove(move)
nextMoves = gameState.getValidMoves()
score = -1 * RecursiveAlphaBetaPruning(gameState, nextMoves,
depth=depth - 1,
alpha=-beta,
beta=-alpha,
turnMultiplier=-1*turnMultiplier)
if score > maxScore:
maxScore = score
if depth == DEPTH:
nextMove = move
gameState.undoMove()
if maxScore > alpha:
alpha = maxScore
if alpha >=beta:
break
return maxScore