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https://github.com/lazocoder/tic-tac-toe
An unbeatable game of Tic Tac Toe.
https://github.com/lazocoder/tic-tac-toe
ai alpha-beta-pruning artificial-intelligence game minimax unbeatable unbeatable-game
Last synced: about 1 month ago
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An unbeatable game of Tic Tac Toe.
- Host: GitHub
- URL: https://github.com/lazocoder/tic-tac-toe
- Owner: LazoCoder
- License: gpl-3.0
- Created: 2016-11-13T21:27:40.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-03-15T00:09:07.000Z (over 7 years ago)
- Last Synced: 2023-03-08T10:27:50.144Z (over 1 year ago)
- Topics: ai, alpha-beta-pruning, artificial-intelligence, game, minimax, unbeatable, unbeatable-game
- Language: Java
- Size: 545 KB
- Stars: 77
- Watchers: 4
- Forks: 42
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tic-Tac-Toe
An unbeatable game of Tic Tac Toe. The AI uses a tweaked Alpha-Beta Pruning algorithm for the decision making.
# Gameplay
The AI cannot be defeated. Every game either ends in a draw or the AI winning.
# What I Learned
## Alpha-Beta Pruning has a "Flaw"
Vanilla Alpha-Beta Pruning sometimes leads to unexpected results such as the following:
You can see in the above gif that the AI could have won by playing an *O* in the bottom left corner but it instead chose the middle cell in the first column. This may seem a little strange but no matter what move I choose afterwards, it still wins.
What I found was that even though Alpha-Beta Pruning based AI plays perfect games, occassionally it chooses to make a move where the outcome will be a slower victory or a quicker loss. By tweaking the algorithm a little bit and including search depth in its board evaluation I was able to get it to always choose the quickest victory or the slowest loss.
The following is what the tweaked Alpha-Beta Pruning algorithm does:
As you can tell, this time it chooses the path of quickest victory.
## Quantity of Boards Evaluated
When using a regular MiniMax algorithm the AI checks **59,704** possible board combinations for its first move (this number obviously decreases throughout the game as less cells are available). The Alpha-Beta Pruning on the other hand brings this number down to **2,337**. Adding the tweak that I mentioned above brings the number up slightly to **2,787**. This increase is miniscule in comparison to the advantages that the algorithm brings to the table.
# Instructions
After downloading, navigate into the folder that contains the packages *ArtificalIntelligence*, *Assets*, *TicTacToe*, and *Results.txt*. Then type the following commands to run the game in Window mode:
```
javac Window.java
java TicTacToe.Window
```
Typing in any parameter will run the game in Player vs. Player mode. Example:
```
java TicTacToe.Window -pvp
```
To run the game in the console, without a GUI, type:
```
java TicTacToe.Console
```
The console version takes in the player input by index. This means that to select position (1, 1), the index would be 4 since it is the 5th square but we are using zero based indexing.
Console mode does not support Player vs. Player.