Tic-Tac-Toe

Tic-Tac-Toe game AI using Minimax algorithm with alpha-beta pruning optimization.
Heuristic/Evaluation function is such that it chooses the move that makes fastest win / slowest loss by giving score to the terminal states based on the their depth.

Features: ✨

1. User-friendly UI/UX with very smooth sliding animations.
2. Choose your own marker (X/O).
3. 2 modes - Play against AI 🤖 or Play against a Friend 🙋🏻‍♂️.
4. 3 difficulty levels for playing against AI.
5. Number of matches played, timer and scoreboard with the playing player highlighted.
6. Strip message animation.

Compatibility: 🖥️ 📱

Responsive design, can be played on any device.

Difficulty Levels (AI): 🎚️

1. Easy:

AI randomly chooses any available spot on the board.

2. Medium:

Minimax algorithm with 30% randomness. AI can choose a random spot with 30% probability.

3. Impossible:

Minimax algorithm with alpha-beta pruning for optimization of minimax algorithm as it reduces the time complexity by pruning nodes in the minimax tree (abstract). Score to a terminal state is assigned with +- depth of that state which accounts for the closness of this terminal state to the current state. Closser terminal states means faster winning move.

Some very good resources: 📚

• Algorithms Explained – minimax and alpha-beta pruning by Sebastian Lague (YouTube)
• GeeksforGeeks' articles on "Minimax Algorithm in Game Theory" - Set 1 | Set 2 | Set 3 | Set 4
• Minimax algorithm implementation - freecodecamp (Article)
• Tic Tac Toe Project Tutorial - freecodecamp (YouTube)

Screenshots: 📷

Main Menu

Game Screen