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This Python-based Tic Tac Toe game features an AI opponent that uses adversarial search with the minimax algorithm and alpha-beta pruning. Experience strategic gameplay as the AI makes optimal moves to challenge players, ensuring a competitive and engaging experience.
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A Tic-Tac-Toe GUI application with an AI opponent.
Tic-Tac-Toe is a two-player, turn-based game. The actions and win conditions are straightforward and simple to understand. Two players (each represented by X and O) take turns marking the spaces on a 3x3 grid. The player who succeeds in placing three of their markers in a horizontal, vertical, or diagonal row wins the game [1].
The possible outcome for a player is a win, loss or draw. A win for one player implies a loss for the other; for this reason, Tic-Tac-Toe is a zero-sum game.
Each player's goal state is a win. As such, the player's goals are competing, which leads to an adversarial search problem.
To run this application, you need to install Python3 and pygame. Assuming Python3 is installed, to install pygame run:
$ pip3 install pygame
or
$ pip3 install -r requirements.txt
To execute the game then run:
$ cd path/to/tic-tac-toe-ai/src
$ python3 run_tic_tac_toe.py
This will launch a pygame window.
The terminal will prompt the choice of AI:
Choose AI. [1/2/3/4]
1. Random
2. Minimax
3. Alpha-beta
4. Alpha-beta with Cutoff
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Once the AI is chosen, it's off the races. Place your marker (X) and the AI will place its marker (O) until a terminal state is found.
When the game is finished, you can then choose to play again.
Play again? [Y/N]
>
This project implements different AI search algorithms:
The minimax algorithm searches the entire game tree and yields a perfect decision for the AI; minimax is unbeatable.
Minimax searches until a terminal state is found. When a terminal state is found, minimax returns the utility of that state {1, 0, -1}. Minimax maximizes the main player's utility while minimizing the utility of the opponent.
Alpha-beta prunes prunes away large and unecessary parts of the minimax game tree, so it too yields an optimal decision. Both minimax and alpha-beta pruning require searching all the way to the terminal states (for at least one portion of the tree).
Because the Tic-Tac-Toe solution space is relatively small (9! = 362,880), it is possible to employ an optimal decision. For more complicated games searching the whole tree is not possible (such as Chess). An evaluation function can be implemented to approximate the best decision for the AI.
In my application, I stop the alpha-beta search at a depth of 3 and call the following evaluation function, f(n), which I made up:
Look at all move combinations for each player in the next two moves:
f(n) = 1, # wins O > # wins X
f(n) = 0, # wins O = # wins X
f(n) = -1, # wins X > # wins O
This evaluation function actually performs very well. While it is not unbeatable, it approximates an intelligent AI in less time.
This project highlights the tradeoffs in time complexity and space complexity between optimal and imperfect AI search algorithms.
The time for the AI to returns its best, first move is shown below according to the search algorithm.
| Search Algorithm | Time to Decision [s] |
|---|---|
| Random | 0.017685 |
| Minimax | 0.495434 |
| Alpha-Beta | 0.049923 |
| Alpha-Beta with Cutoff | 0.022828 |
Minimax takes nearly half a second to place its first move! Alpha-beta shaves this number down by a factor of 10. Alpha-beta with cutoff does even better.
[2] Russell, Stuart J., and Peter Norvig. Artificial Intelligence: a Modern Approach. Pearson, 2010.