A minmax search with alpha-beta pruning "library". Comes with TicTacToe, Connect4, and Checkers AIs using said "library".

Import minmax_ai and create an AI instance. WHen you want a move, call get_move with the current state.

import minmax_ai

opp = 'O'
player = 'X'

...

ai = minmax_ai.AI(ai_piece=opp,
                  opp=player,
                  depth=9,
                  game_over_fun=game_over,
                  eval_fun=evaluate,
                  moves_fun=possible_moves,
                  next_state_fun=next_state)

move = ai.get_move(current_state)

• ai_piece : However you represent the AI
• opp: However you represent the AI's opponent the human player in most cases
• depth : An integer. How many moves ahead you want the AI to look. The larger the depth, the longer the AI takes to think.
• game_over_fun : a function determining if a game is over
• eval_fun : a static evaluation function for "scoring" a given state from the player's perspective (not the AI)
• moves_fun : a function to generate all possible moves given a state
• next_state_fun : a function to generate a new state given an old state, the player whose turn it is, and the move

Contract for the four functions (Haskell-style):
game_over_fun :: board -> player -> opponent -> boolean
eval_fun :: board -> player -> opponent -> number
moves_fun :: board -> player -> [move]
next_state_fun :: board -> player -> move -> board

It doesn't matter what type of thing move is but it must be same thing that next_state_fun accepts.

A peek at the checkers AI. I thought it was being stupid by giving me a free king but very soon realized I was set up for a double jump. :sadface: