To play the Othello game, run with command python3 othello_gui.py -d [dimension], where [dimension] can be placed with the size of the board. Providing a single number is enough since the board is square.
To play against my minimax algorithm agent, run `python3 othello_gui.py -d [dimension] -a agent.py -m".
To play against my alpha-beta algorithm agent, run python3 othello_gui.py -d [dimension] -a agent.py.
To play with a depth limit, add a -l [depth_limit] at the end of the command. Ex: python3 othello_gui.py -d [dimension] -a agent.py -m -l [depth limit], where you can choose the depth limit.
To play with caching states, add a -c at the end. Ex: python3 othello_gui.py -d [dimension] -a agent.py -m -c.
To play with node ordering heuristics and caching states, add a -o at the end. Ex: python3 othello_gui.py -d [dimension] -a agent.py -c -o.
(This project is based on one of Columbia University’s Artificial Intelligence Courses(author: Daniel Bauer))