def minvalue(state, maxdepth, alpha=None, beta=None):
"""
The minvalue function for the adversarial tree search.
"""
board = state[0]
turn = state[1]
if is_terminal(state, maxdepth):
return utility(state)
else:
v = float('inf')
(moves, captures) = crank.get_hints(board, turn)
if captures:
for a in captures:
v = min(v, maxvalue(transition(state, a, "jump"), \
maxdepth, alpha, beta))
if alpha is not None and beta is not None:
if v <= alpha:
return v
beta = min(beta, v)
return v
elif moves:
for a in moves:
v = min(v, maxvalue(transition(state, a, "move"), \
maxdepth, alpha, beta))
if alpha is not None and beta is not None:
if v <= alpha:
return v
beta = min(beta, v)
return v
def is_terminal(state, maxdepth=None):
"""
Determines if a tree node is a terminal or not.
Returns boolean True/False.
"""
board = state[0]
turn = state[1]
depth = state[2]
(moves, captures) = crank.get_hints(board, turn)
if maxdepth is not None:
return ((not moves) and (not captures)) or depth >= maxdepth
else:
return ((not moves) and (not captures))
def minimax_search(state, maxdepth=None):
"""
The depth limited minimax tree search.
"""
board = state[0]
turn = state[1]
(moves, captures) = crank.get_hints(board, turn)
if captures:
return max([(a, minvalue(transition(state, a, "jump"), maxdepth)) \
for a in captures], key = lambda v: v[1])
elif moves:
return max([(a, minvalue(transition(state, a, "move"), maxdepth)) \
for a in moves], key = lambda v: v[1])
else:
return ("pass", -1)
def alphabeta_search(state, maxdepth=None):
"""
The depth limited alpha-beta tree search, it's 2-times faster than
the minimax search.
"""
board = state[0]
turn = state[1]
(moves, captures) = crank.get_hints(board, turn)
alpha = float('-inf')
beta = float('inf')
if captures:
return max([\
(a, minvalue(transition(state, a, "jump"), \
maxdepth, alpha, beta)) \
for a in captures], key = lambda v: v[1])
elif moves:
return max([\
(a, minvalue(transition(state, a, "move"), \
maxdepth, alpha, beta)) \
for a in moves], key = lambda v: v[1])
else:
return ("pass", -1)