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Amazingly Faster and Accurate
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Aditya-y9 committed Nov 5, 2023
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1 change: 0 additions & 1 deletion .idea/workspace.xml
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114 changes: 60 additions & 54 deletions ChessAI/AI.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,14 +13,15 @@
# the score will be based on the number of moves available
# the score will be based on the checkmate and stalemate
# the score will be based on the check
import time
import random
import engine
from engine import gamestate
import numpy
import numpy as np
global KingNeighbourPawns
KingNeighbourPawns = 0
# to store material values of the pieces
pieceScore = {"K": 100, "Q": 9, "R": 5, "B": 3, "N": 3, "p": 0.2
pieceScore = {"K": 0, "Q": 1025, "R": 477, "B": 365, "N": 337, "p": 82
}


Expand All @@ -35,53 +36,54 @@

STALEMATE = 0

DEPTH = 2


knightScores = [[0.0, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.0],
[0.1, 0.3, 0.5, 0.5, 0.5, 0.5, 0.3, 0.1],
[0.2, 0.5, 0.6, 0.65, 0.65, 0.6, 0.5, 0.2],
[0.2, 0.55, 0.65, 0.7, 0.7, 0.65, 0.55, 0.2],
[0.2, 0.5, 0.65, 0.7, 0.7, 0.65, 0.5, 0.2],
[0.2, 0.55, 0.6, 0.65, 0.65, 0.6, 0.55, 0.2],
[0.1, 0.3, 0.5, 0.55, 0.55, 0.5, 0.3, 0.1],
[0.0, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.0]]

bishopScores = [[0.0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.0],
[0.2, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.2],
[0.2, 0.4, 0.5, 0.6, 0.6, 0.5, 0.4, 0.2],
[0.2, 0.5, 0.5, 0.6, 0.6, 0.5, 0.5, 0.2],
[0.2, 0.4, 0.6, 0.6, 0.6, 0.6, 0.4, 0.2],
[0.2, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.2],
[0.2, 0.5, 0.4, 0.4, 0.4, 0.4, 0.5, 0.2],
[0.0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.0]]

rookScores = [[0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25],
[0.5, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.5],
[0.0, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.0],
[0.0, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.0],
[0.0, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.0],
[0.0, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.0],
[0.0, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.0],
[0.25, 0.25, 0.25, 0.5, 0.5, 0.25, 0.25, 0.25]]

queenScores = [[0.0, 0.2, 0.2, 0.3, 0.3, 0.2, 0.2, 0.0],
[0.2, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.2],
[0.2, 0.4, 0.5, 0.5, 0.5, 0.5, 0.4, 0.2],
[0.3, 0.4, 0.5, 0.5, 0.5, 0.5, 0.4, 0.3],
[0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.4, 0.3],
[0.2, 0.5, 0.5, 0.5, 0.5, 0.5, 0.4, 0.2],
[0.2, 0.4, 0.5, 0.4, 0.4, 0.4, 0.4, 0.2],
[0.0, 0.2, 0.2, 0.3, 0.3, 0.2, 0.2, 0.0]]

pawnScores = [[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
[0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7],
[0.3, 0.3, 0.4, 0.5, 0.5, 0.4, 0.3, 0.3],
[0.25, 0.25, 0.3, 0.45, 0.45, 0.3, 0.25, 0.25],
[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[0.25, 0.15, 0.1, 0.2, 0.2, 0.1, 0.15, 0.25],
[0.25, 0.3, 0.3, 0.0, 0.0, 0.3, 0.3, 0.25],
[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]]
DEPTH = 3


knightScores = np.array([[-167, -89, -34, -49, 61, -97, -15, -107],
[-73, -41, 72, 36, 23, 62, 7, -17],
[-47, 60, 37, 65, 84, 129, 73, 44],
[ -9, 17, 19, 53, 37, 69, 18, 22],
[-13, 4, 16, 13, 28, 19, 21, -8],
[-23, -9, 12, 10, 19, 17, 25, -16],
[-29, -53, -12, -3, -1, 18, -14, -19],
[-105, -21, -58, -33, -17, -28, -19, -23]])

bishopScores = np.array([[-29, 4, -82, -37, -25, -42, 7, -8],
[-26, 16, -18, -13, 30, 59, 18, -47],
[-16, 37, 43, 40, 35, 50, 37, -2],
[-4, 16, 13, 28, 19, 47, 15, -14],
[-6, 5, 13, 13, 13, 12, 4, -9],
[0, 15, 15, 15, 14, 27, 18, -18],
[-4, 15, 14, 15, 14, 15, 10, -18],
[-19, 7, -8, -19, -11, -16, 7, -26]])


rookScores = np.array([[32, 42, 32, 51, 63, 9, 31, 43],
[27, 32, 58, 62, 80, 67, 26, 44],
[-5, 19, 26, 36, 17, 45, 61, 16],
[-24, -11, 7, 26, 24, 35, -8, -20],
[-36, -26, -12, -1, 9, -7, 6, -23],
[-45, -25, -16, -17, 3, 0, -5, -33],
[-44, -16, -20, -9, -1, 11, -6, -71],
[-19, -13, 1, 17, 16, 7, -37, -26]])

queenScores = np.array([[ -28, 0, 29, 12, 59, 44, 43, 45],
[-24, -39, -5, 1, -16, 57, 28, 54],
[-13, -17, 7, 8, 29, 56, 47, 57],
[-27, -27, -16, -16, -1, 17, -2, 1],
[ -9, -26, -9, -10, -2, -4, 3, -3],
[-14, 2, -11, -2, -5, 2, 14, 5],
[-35, -8, 11, 2, 8, 15, -3, 1],
[ -1, -18, -9, 10, -15, -25, -31, -50]])

pawnScores = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0],
[98, 134, 61, 95, 68, 126, 34, -11],
[-6, 7, 26, 31, 65, 56, 25, -20],
[-14, 13, 6, 21, 23, 12, 17, -23],
[-27, -2, -5, 12, 17, 6, 10, -25],
[-26, -4, -4, -10, 3, 3, 33, -12],
[-35, -1, -20, -23, -15, 24, 38, -22],
[0, 0, 0, 0, 0, 0, 0, 0]])

piecePositionScores = {"wN": knightScores,
"bN": knightScores[::-1],
Expand Down Expand Up @@ -209,14 +211,15 @@ def findRandomMove(validMoves,gs):
'''

def findBestMove(gs, validMoves):
start = time.time()
global nextMove , counter
# if we dont have next move
# we will find the next move by random
nextMove = None
random.shuffle(validMoves)
counter = 0
# findMoveNegaMax(gs, validMoves, DEPTH, 1 if gs.whitemove else -1)
findMoveNegaMaxAlphaBeta(gs, validMoves, DEPTH,-CHECKMATE,CHECKMATE, 1 if gs.whitemove else -1)
findMoveNegaMaxAlphaBeta(gs, validMoves, DEPTH,-CHECKMATE,CHECKMATE, 1 if gs.whitemove else -1,start)
print(counter)
return nextMove

Expand Down Expand Up @@ -255,7 +258,7 @@ def findMoveMinMax(gs, validMoves, depth, whiteToMove):
gs.undoMove()
return minScore

def findMoveNegaMaxAlphaBeta(gs, validMoves, depth, alpha, beta, turnMultiplier):
def findMoveNegaMaxAlphaBeta(gs, validMoves, depth, alpha, beta, turnMultiplier,start):
# alpha beta pruning
global counter
counter += 1
Expand All @@ -275,7 +278,7 @@ def findMoveNegaMaxAlphaBeta(gs, validMoves, depth, alpha, beta, turnMultiplier)
# during recursion

# because for our opponent alpha and beta will be inverted
score = -findMoveNegaMaxAlphaBeta(gs, nextMoves, depth - 1,-beta,-alpha,-turnMultiplier)
score = -findMoveNegaMaxAlphaBeta(gs, nextMoves, depth - 1,-beta,-alpha,-turnMultiplier,start)
# if maxScore is greater than beta
# we will prune the tree
# if we get better move evlauation score
Expand All @@ -291,6 +294,8 @@ def findMoveNegaMaxAlphaBeta(gs, validMoves, depth, alpha, beta, turnMultiplier)
alpha = maxScore
if alpha >= beta:
break
if time.time() - start > 6:
break
# we stop looking at the next moves
return maxScore

Expand Down Expand Up @@ -360,9 +365,10 @@ def ScoreBoard(gs):
if square[1] != "K":
piece_position_score = piecePositionScores[square][row][col]
if square[0] == "w":
score += pieceScore[square[1]] + piece_position_score + 0.6*int(gs.wcastled)+0.2*int(len(gs.getvalidmoves()))+0*int(KingPawnShield(gs))+(-0.7)*int(doublePawns(gs))+0.03*int(len(engine.Queen_squares))+0.3*int(countWhitePiecesOnKingSurroundingSquares(gs))+0.3*int(len(engine.King_squares))+0.02*int(centrePawnCount(gs))+0.2*int(rookOnSeventh(gs))+0.2*int(bishopOnLarge(gs))+0.2*int(knightSupport(gs))
score += pieceScore[square[1]] + piece_position_score

if square[0] == "b":
score -= (pieceScore[square[1]] + piece_position_score +0.6*int(gs.wcastled)+0.2*int(len(gs.getvalidmoves()))+0.3*int(KingPawnShield(gs))+(-0.7)*int(doublePawns(gs))+0.03*int(len(engine.Queen_squares))+0*int(countWhitePiecesOnKingSurroundingSquares(gs))+0.3*int(len(engine.King_squares))+0.02*int(centrePawnCount(gs))+0.2*int(rookOnSeventh(gs))+0.2*int(bishopOnLarge(gs))+0.2*int(knightSupport(gs)))
score -= (pieceScore[square[1]] + piece_position_score )

return score

Expand Down
Binary file modified ChessAI/__pycache__/AI.cpython-310.pyc
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1 change: 1 addition & 0 deletions ChessAI/main.py
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Expand Up @@ -7,6 +7,7 @@
# import tester as tt
# import gtts
import time
import chess

# # this function will read all persons' training images, detect face from each image
# # used to load an image from a file
Expand Down

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