Final project of the course Introduction to Artificial Intelligence of NCTU.
This project implements an Othello game engine and several different AIs. In particular, the following approaches are used.
- Greedy, which selects the action maximising the heuristic evaluation function
- Minimax, with or without alpha-beta pruning
- Expectimax
- Pure MCTS
There is also a graphical interface where you can play against these AIs with three levels of difficulty.
- Python 3.9 or newer
GUI code is modified from johnafish's design
python3 graphical_interface.py
This section serves as a quick overview of the Python API. For details, refer to the documentation in the source code.
Everything related to game mechanics reside in the othello module.
The Game class represents an Othello game.
It keeps track of the game state (the state field) and which player plays next (the next_player field).
The play method plays a given move on behalf of a given player.
game = othello.Game() # Initialise a new game.
game.play(othello.Player.DARK, othello.Action(othello.Coords.from_repr('d3'))) # Dark plays d3.
game.play(othello.Player.LIGHT, othello.Action(othello.Coords.from_repr('c5'))) # Light plays c5.The get_conclusion method returns the conclusion of the game.
I.e., whether the game is still progressing, the game ends in a draw, or either player wins.
game = othello.Game()
assert game.get_conclusion() is None # The game is still progressing.The State class represents the state of an Othello game.
It contains the current board configuration (the board field).
The initial static method returns the initial state.
The get_legal_actions method returns an iterable of all legal actions for a given player.
state = othello.State.initial()
assert set(game.state.get_legal_actions(othello.Player.DARK)) == {othello.Action(othello.Coords.from_repr(a)) for a in ('d3', 'c4', 'f5', 'e6')}The perform_action method performs a given action on behalf of a given player.
This is usually used in simulation (e.g. as used in search algorithms).
Note that this method returns a new state, rather than doing an in-place update.
state = othello.State.initial()
next_state = state.perform_action(othello.player.DARK, othello.Action(othello.Coords.from_repr('d3'))) # Dark plays d3.The is_terminal method checks if the state is terminal.
state = othello.State.initial()
assert not state.is_terminal()The Board class represents a board configuration of an Othello game.
Refer to the documentation in the source code for its representation.
The initial static method returns the initial configuration.
The content of a particular square can be accessed by using the index operator.
board = othello.Board.initial()
assert board[othello.Coords.from_repr('d4')] is othello.Player.DARK # d4 has a dark piece.
assert board[othello.Coords.from_repr('d3')] is None # d3 is empty.The set method sets the content of a particular square.
Note that this method returns a new board, rather than doing an in-place update.
board = othello.Board.initial()
assert board[othello.Coords.from_repr('d3')] is None
new_board = board.set(othello.Coords.from_repr('d3'), othello.player.LIGHT)
assert new_board[othello.Coords.from_repr('d3')] is othello.player.LIGHTThese classes reside in the othello module.
The Agent class is the base class for an agent. An agent inherits this class and override the play method.
See the random agent for an example.
This class is used by the Referee class to automatically run the game.
The constructor of the Referee class takes two instances of Agent.
The Referee class contains an instance of Game (the game field) which handles all game logic.
All information related to the game can be accessed through it.
referee = othello.Referee(dark_agent, light_agent)
referee.run()
print(referee.game.get_conclusion()) # Who wins the game?The Referee class provides callbacks to be called at certain times. To attach callbacks, inherit this class.
See the log referee, which logs the game progress to stdout, for an example.