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David_AI_v7.py
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"""This chess engine was written by David for fun. A board is represented by a [str] representing a 2D board.
ToDo:
- switch to negamax
- change positional scoring according to the game's phase
- discount future scores
- castling
- en passant
- aspiration search
-
note that the cscore only includes parts of the score that are cumulatively evaluated
score is the result of the evaluation function
"""
from time import perf_counter as now
import copy
from shared import ThreeFoldRepetition
PIECE_MOVE_DIRECTION = {
'K': ((1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0), (-1, -1), (0, -1), (1, -1)),
'k': ((1, 0), (0, 1), (-1, 0), (0, -1), (1, 1), (1, -1), (-1, 1), (-1, -1)),
'Q': ((1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0), (-1, -1), (0, -1), (1, -1)),
'q': ((1, 0), (0, 1), (-1, 0), (0, -1), (1, 1), (1, -1), (-1, 1), (-1, -1)),
'R': ((1, 0), (0, 1), (-1, 0), (0, -1)),
'r': ((1, 0), (0, 1), (-1, 0), (0, -1)),
'B': ((1, 1), (1, -1), (-1, 1), (-1, -1)),
'b': ((1, 1), (1, -1), (-1, 1), (-1, -1)),
'N': ((1, 2), (2, 1), (2, -1), (1, -2), (-1, -2), (-2, -1), (-2, 1), (-1, 2)),
'n': ((1, 2), (2, 1), (2, -1), (1, -2), (-1, -2), (-2, -1), (-2, 1), (-1, 2)),
}
PIECE_VALUE = {
'.': 0,
'K': 20000, 'Q': 975, 'R': 500, 'B': 335, 'N': 325, 'P': 100,
'k': -20000, 'q': -975, 'r': -500, 'b': -335, 'n': -325, 'p': -100
}
POSITION_VALUE_READABLE = {
'P': [
[0, 0, 0, 0, 0, 0, 0, 0],
[50, 50, 50, 50, 50, 50, 50, 50],
[10, 10, 20, 30, 30, 20, 10, 10],
[5, 5, 10, 25, 25, 10, 5, 5],
[0, 0, 0, 2, 2, 0, 0, 0],
[5, -5,-10, 0, 0,-10, -5, 5],
[5, 10, 10,-20,-20, 10, 10, 5],
[0, 0, 0, 0, 0, 0, 0, 0]],
# [[5*(x - (x * x / 7))+(0.02 * (y+2)**4)-10 for x in range(8)] for y in range(7, -1, -1)],
# print('\n'.join(' '.join('{}'.format(int(PAWN_POSITION_VALUE[y][x]))
# for x in range(8))for y in range(8))+'\n')
'N': [
[-8, -8, -8, -8, -8, -8, -8, -8],
[-8, 0, 0, 0, 0, 0, 0, -8],
[-8, 0, 4, 6, 6, 4, 0, -8],
[-8, 0, 6, 8, 8, 6, 0, -8],
[-8, 0, 6, 8, 8, 6, 0, -8],
[-8, 0, 4, 6, 6, 4, 0, -8],
[-8, 0, 1, 2, 2, 1, 0, -8],
[-16,-12, -8, -8, -8, -8, -12, -16]],
'B': [
[-4, -4, -4, -4, -4, -4, -4, -4],
[-4, 0, 0, 0, 0, 0, 0, -4],
[-4, 0, 2, 4, 4, 2, 0, -4],
[-4, 0, 4, 6, 6, 4, 0, -4],
[-4, 0, 4, 6, 6, 4, 0, -4],
[-4, 1, 2, 4, 4, 2, 1, -4],
[-4, 2, 1, 1, 1, 1, 2, -4],
[-4, -4, -12, -4, -4, -12, -4, -4]],
'R': [
[5, 5, 5, 5, 5, 5, 5, 5],
[-5, 0, 0, 0, 0, 0, 0, -5],
[-5, 0, 0, 0, 0, 0, 0, -5],
[-5, 0, 0, 0, 0, 0, 0, -5],
[-5, 0, 0, 0, 0, 0, 0, -5],
[-5, 0, 0, 0, 0, 0, 0, -5],
[-5, 0, 0, 0, 0, 0, 0, -5],
[0, 0, 0, 2, 2, 0, 0, 0]],
'Q': [
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 2, 2, 1, 0, 0],
[0, 0, 2, 3, 3, 2, 0, 0],
[0, 0, 2, 3, 3, 2, 0, 0],
[0, 0, 1, 2, 2, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[-5, -5, -5, -5, -5, -5, -5, -5]],
'K': [
[-40, -30, -50, -70, -70, -50, -30, -40],
[-30, -20, -40, -60, -60, -40, -20, -30],
[-20, -10, -30, -50, -50, -30, -10, -20],
[-10, 0, -20, -40, -40, -20, 0, -10],
[0, 10, -10, -30, -30, -10, 10, 0],
[10, 20, 0, -20, -20, 0, 20, 10],
[30, 40, 20, 0, 0, 20, 40, 30],
[40, 50, 30, 10, 10, 30, 50, 40]],
'.': [[0 for _ in range(8)] for _ in range(8)]
}
POSITION_VALUE = dict()
for piece_ in POSITION_VALUE_READABLE:
tmp = copy.deepcopy(POSITION_VALUE_READABLE[piece_])
POSITION_VALUE[piece_.lower()] = [[-value for value in row] for row in tmp]
tmp.reverse()
POSITION_VALUE[piece_] = tmp
transpositionTable = dict()
total_moves = 0
time_out_point = now() + 100
def evaluate(_board: [str])->float:
_score = 0.0
for y in range(8):
for x in range(8):
piece = _board[y][x]
_score += PIECE_VALUE[piece]
_score += POSITION_VALUE[piece][y][x]
return _score
def piece_count(board):
total = 0
for row in board:
for piece in row:
if piece != '.':
total += 1
return total
def move(board: [str], y1, x1, y2, x2)-> [str]:
global total_moves
"""returns a board with a move made"""
total_moves += 1
board = board.copy()
# add piece to destination
board[y2] = board[y2].copy()
board[y2][x2] = board[y1][x1]
# remove piece from source
board[y1] = board[y1].copy()
board[y1][x1] = '.'
return board
def moves(board: [str], _player_is_white: bool):
"""This generates a list of all possible game states after one move.
Preferred moves should be later in the returned list."""
for x in range(8):
for y in range(8):
piece = board[y][x]
if piece in 'KQRBN' if _player_is_white else piece in 'kqrbn':
for xd, yd in PIECE_MOVE_DIRECTION[piece]:
for i in range(1, 100):
x2 = x+i*xd
y2 = y+i*yd
if not (0 <= x2 <= 7 and 0 <= y2 <= 7):
# then it is a move off the board
break
target_piece = board[y2][x2]
if target_piece == '.':
# then it is moving into an empty square
yield (
move(board, y, x, y2, x2),
POSITION_VALUE[piece][y2][x2] -
POSITION_VALUE[piece][y][x])
elif target_piece.islower() if _player_is_white else target_piece.isupper():
# then it is taking an opponent's piece
yield (
move(board, y, x, y2, x2),
POSITION_VALUE[piece][y2][x2] -
POSITION_VALUE[target_piece][y2][x2] -
POSITION_VALUE[piece][y][x] -
PIECE_VALUE[target_piece])
break
else:
# then it is taking it's own piece
break
if piece in 'KkNn':
break
# pawns are weird
if piece == 'P' if _player_is_white else piece == 'p':
y2 = y+1 if _player_is_white else y-1
# check if a take is possible
for x2 in (x - 1, x + 1):
if 0 <= x2 <= 7:
target_piece = board[y2][x2]
if target_piece.islower() if _player_is_white else target_piece.isupper():
# then a take is possible
after_pawn_move = move(board, y, x, y2, x2)
if y2 == 7 if _player_is_white else y2 == 0:
# then the end of the board has been reached and promotion is needed
for replacement_piece in ('QRBN' if _player_is_white else 'qrbn'):
after_pawn_replacement = after_pawn_move.copy()
after_pawn_replacement[y2] = after_pawn_replacement[y2].copy()
after_pawn_replacement[y2][x2] = replacement_piece
yield(
after_pawn_replacement,
PIECE_VALUE[replacement_piece] -
PIECE_VALUE[target_piece] -
PIECE_VALUE[piece] +
POSITION_VALUE[replacement_piece][y2][x2] -
POSITION_VALUE[target_piece][y2][x2] -
POSITION_VALUE[piece][y][x])
else:
yield(
after_pawn_move,
POSITION_VALUE[piece][y2][x2] -
POSITION_VALUE[target_piece][y2][x2] -
POSITION_VALUE[piece][y][x] -
PIECE_VALUE[target_piece])
# check if pawn can move forwards 1
if board[y2][x] == '.':
# check if pawn can be promoted
if y2 == 7 if _player_is_white else y2 == 0:
after_pawn_move = move(board, y, x, y2, x)
# add each possible promotion to _moves
for replacement_piece in ('QRBN' if _player_is_white else 'qrbn'):
after_pawn_replacement = after_pawn_move.copy()
after_pawn_replacement[y2] = after_pawn_replacement[y2].copy()
after_pawn_replacement[y2][x] = replacement_piece
yield(
after_pawn_replacement,
PIECE_VALUE[replacement_piece] -
PIECE_VALUE[piece] +
POSITION_VALUE[replacement_piece][y2][x] -
POSITION_VALUE[piece][y][x])
else:
yield(
move(board, y, x, y2, x),
POSITION_VALUE[piece][y2][x] -
POSITION_VALUE[piece][y][x])
# check if pawn can move forwards 2
if y == 1 if _player_is_white else y == 6:
y2 = y + 2 if _player_is_white else y - 2
if board[y2][x] == '.':
yield(
move(board, y, x, y2, x),
POSITION_VALUE[piece][y2][x] -
POSITION_VALUE[piece][y][x])
def alpha_beta(board, depth, current_cscore, player_is_white, alpha, beta)->int:
"""Implements alpha beta tree search, returns a score. This fails soft."""
# assert abs(current_cscore - evaluate(board)) < 0.001
# lookup the current node to see if it has already been searched
key = ''.join(piece for row in board for piece in row) + ('w' if player_is_white else 'b')
if key in transpositionTable:
node_score, node_type, node_search_depth = transpositionTable[key]
if node_search_depth >= depth:
if (node_type == 'exact' or
node_type == 'high' and node_score >= beta or
node_type == 'low' and node_score <= alpha):
return node_score
possible_moves = moves(board, player_is_white)
if depth > 1:
if now() > time_out_point:
raise TimeoutError
# then try to guess the best order to try moves
possible_moves = list(possible_moves)
possible_moves.sort(key=lambda _move: _move[1], reverse=player_is_white)
if not possible_moves:
# this correctly scores stalemates
# it only works on lists, not generators
return 0
current_best_score = (-99999) if player_is_white else 99999
for possible_move, diff in possible_moves:
move_score = current_cscore + diff
# assert abs(move_score - evaluate(possible_move)) < 0.001
# Only search deeper if both kings are still present.
# This also stops my engine trading my king now for your king later.
# I also search deeper then normal if a take is made
# Note that the comparison is ordered for evaluation speed
if depth >= 1 and (depth >= 2 or abs(diff) > 50) and abs(diff) < 1000:
# this does not always use move ordering :-( todo
move_score = alpha_beta(possible_move, depth - 1, move_score, not player_is_white, alpha, beta)
if player_is_white:
if move_score > current_best_score:
current_best_score = move_score
if move_score > alpha:
alpha = move_score
if alpha >= beta:
# the score failed high
transpositionTable[key] = current_best_score, 'high', depth
break
else:
if move_score < current_best_score:
current_best_score = move_score
if move_score < beta:
beta = move_score
if alpha >= beta:
# the score failed low
transpositionTable[key] = current_best_score, 'low', depth
break
else:
# the score is exact and the earlier check of the table ensures that we are not overwriting
# an entry of greater depth
transpositionTable[key] = current_best_score, 'exact', depth
assert key in transpositionTable
return current_best_score
def estimated_score(board, previous_cscore, diff, player_is_white):
key = ''.join(piece for row in board for piece in row) + ('w' if player_is_white else 'b')
if key in transpositionTable:
return transpositionTable[key][0]
else:
return previous_cscore + diff
def search(possible_moves, depth, current_cscore, player_is_white, alpha, beta):
"""Implements top level node in alpha_beta tree search, returns a best move"""
# assert depth > 0
possible_moves.sort(
key=lambda _move: estimated_score(_move[0], current_cscore, _move[1], player_is_white),
reverse=player_is_white)
for possible_move, diff in possible_moves:
if depth == 1:
move_score = current_cscore + diff
# assert abs(move_score - evaluate(possible_move)) < 0.001
else:
move_score = alpha_beta(possible_move, depth - 1, current_cscore + diff, not player_is_white, alpha, beta)
if player_is_white:
if move_score > alpha:
alpha = move_score
best_move = possible_move
else:
if move_score < beta:
beta = move_score
best_move = possible_move
return best_move, alpha if player_is_white else beta
def main(history, white_time, black_time):
global transpositionTable
global time_out_point
transpositionTable = dict()
start_time = now()
player_is_white = len(history) % 2 == 1
available_time = white_time if player_is_white else black_time
time_out_point = start_time + available_time - 0.5 # always hold 0.5 seconds in reserve
# history = [[''.join(row) for row in board] for board in history]
current_score = evaluate(history[-1])
possible_moves = list(moves(history[-1], player_is_white))
if (current_score < -1100) if player_is_white else (current_score > 1100):
# if I am losing badly and in a loop then call a draw
if len(history) > 9 and history[-1] == history[-5] == history[-9]:
raise ThreeFoldRepetition
else:
# otherwise avoid repeated states
repeat_free_moves = [m for m in possible_moves if m[0] not in history]
if repeat_free_moves:
# only remove repeats if there are still choices remaining
possible_moves = repeat_free_moves
best_move = None
alpha = -99999
beta = 99999
# 5 depth search can take 13.149 seconds in worst case seen so far :-(
for depth in range(1, 10):
search_start_time = now()
try:
best_move, best_score = search(possible_moves, depth, current_score, player_is_white, alpha, beta)
except TimeoutError:
print('internal timeout')
break
search_run_time = now() - search_start_time
# print(f'{depth} {search_run_time:.3f}')
time_remaining = available_time - (now() - start_time)
if time_remaining < search_run_time * 30:
break
if abs(best_score) > 10000:
# print('check mate is expected')
break
print(f'search depth: {depth}-{depth+1}')
print(f'expected score: {best_score}')
return best_move # [[piece for piece in line] for line in best_move]
'''
At this point David_AI_v4 wins 16/16 games
changed position scoring to make David_AI_v5
this also causes the search of more moves for the benchmark position
250 2 0.002
5160 3 0.046
18017 4 0.090
217693 5 1.385
737830 6 3.568
144904 moves searched per second
small boost from removing unnecessary code
250 2 0.002
5160 3 0.050
18017 4 0.138
217693 5 1.092
737965 6 2.868
177823 moves searched per second
added move counting
300 2 0.083
3508 3 0.483
12901 4 1.628
109606 5 13.375
396902 6 47.114
6331 moves searched per second
switched to centipawn evaluation & tweaked scoring
358 2 0.055
4777 3 0.612
21297 4 2.285
286896 5 36.687
7237 leaves searched per second
15/20 scored with the extra terms
18/20 with extra terms removed
24/26 after conversion to centipawns
23.5/26 with CPW piece square tables (add incentive to move pawns forwards)
166 2 0.002
4876 3 0.040
19911 4 0.097
287690 5 1.339
1105592 6 4.259
192741 leaves searched per second
simplified piece value lookup
159 2 0.002
5164 3 0.033
20096 4 0.086
160780 5 0.809
172953 leaves searched per second
removed function call for position value lookup
159 2 0.001
5164 3 0.024
20096 4 0.094
160780 5 0.669
204022 leaves searched per second :-)
changed pawn lookup table and switched to testing on PC
131 2 0.001
4057 3 0.029
18982 4 0.108
126569 5 0.776
138422 leaves searched per second
added quiescence search to depth 2
878 2 0.008
45827 3 0.336
197728 4 1.433
1102823 5 6.893
127180 leaves searched per second
ditched fancy move sorting
878 2 0.008
44478 3 0.332
190035 4 1.300
1032192 5 6.660
124355 leaves searched per second
removed all traces of evaluate
878 2 0.008
45726 3 0.312
194771 4 1.297
1094450 5 6.508
134704 leaves searched per second
quiescence search to depth 1
210 2 0.002
8224 3 0.068
58484 4 0.354
311780 5 2.244
116886 leaves searched per second
switched back to mac
210 2 0.003
8224 3 0.056
58484 4 0.311
311780 5 2.356
114390 leaves searched per second
reordered comparison
210 2 0.002
8224 3 0.087
58484 4 0.218
311780 5 1.363
186732 leaves searched per second
switched back to using lists of lists
210 2 0.002 65
8224 3 0.059 1977
58484 4 0.226 3889
311780 5 1.751 59593
152995 leaves searched per second
'''