src.ai

Submodules

src.ai.ai module

Base AI Player

class src.ai.ai.AIPlayer

Bases: object

Base Class for all AI Computer Opponents

src.ai.minimax module

Minimax Opponent

class src.ai.minimax.MinimaxPlayer(depth=None)

Bases: AIPlayer

Minimax Computer Opponent

Parameters:

depth: int, optional

Maximum search depth for the minimax algorithm. If no depth is specified, infinity (math.inf) will be used in the minimax algorithm. Default is None.

Methods

minimax(grid, depth, maximizing_player)	Minimax algorithm
play(play_fn, game)	Play the AI opponent
score(winner)	Calculate the game score for a winner node

minimax(grid, depth: int, maximizing_player: bool)

Minimax algorithm

Parameters:

grid: Grid

Current game state to calculate the minimax value from.

depth: int

Current search depth of the minimax algorithm.

maximizing_player: bool

Whether the current player is maximizing or minimizing his score.

Returns:

int

Score of the given grid layout.

See also

src.ai.ABPrunedMinimaxPlayer.minimax

minimax with alpha-beta pruning

play(play_fn, game)

Play the AI opponent

Parameters:

play_fn: `callable` [tuple[int, int]]

Play function receiving a board position (x, y) on which to place the AI’s symbol.

game: Game

Game object containing the game logic and the current game state.

Returns:

tuple[int, int]

Position on the board chosen by the algorithm.

score(winner)

Calculate the game score for a winner node

Return the score for a given winner. This will be a positive number if the winner is the AI, and a negative number if the winner is the human.

Parameters:

winner: str or int

Symbol of the winner node. Can also be a 0 if the state is a tie.

Returns:

int

Score of the final game state

src.ai.minimax_abpruning module

Minimax Opponent with Alpha-Beta-Pruning

class src.ai.minimax_abpruning.ABPrunedMinimaxPlayer(depth=None)

Bases: MinimaxPlayer

Alpha-Beta-Pruned Minimax Computer Opponent

Parameters:

depth: int, optional

Maximum search depth for the minimax algorithm. If no depth is specified, infinity (math.inf) will be used in the minimax algorithm. Default is None.

Methods

minimax(grid, depth[, alpha, beta, ...])	Alpha-beta-pruned minimax algorithm
play(play_fn, game)	Play the AI opponent
score(winner)	Calculate the game score for a winner node

minimax(grid, depth: int, alpha: int = None, beta: int = None, maximizing_player: bool = False)

Alpha-beta-pruned minimax algorithm

Parameters:

grid: Grid

Current game state to calculate the minimax value from.

depth: int

Current search depth of the minimax algorithm.

alpha, beta: int

Values used for alpha-beta pruning.

maximizing_player: bool

Whether the current player is maximizing or minimizing his score.

Returns:

int

Score of the given grid layout.

See also

src.ai.MinimaxPlayer.minimax

minimax without alpha-beta pruning

src.ai.random module

Random Opponent

class src.ai.random.RandomPlayer

Bases: AIPlayer

Randomly Playing Computer Opponent

Methods

play(play_fn, game)

Play the AI opponent

play(play_fn, game)

Play the AI opponent

Parameters:

play_fn: `callable` [tuple[int, int]]

Play function receiving a board position (x, y) on which to place the AI’s symbol.

game: Game

Game object containing the game logic and the current game state.

Returns:

tuple[int, int]

Random position on the board chosen by the algorithm.

Module contents

Collection of AI Computer Opponents