A game integrated with a min-max algorithm to make more accurate decisions based on result overviews.

Recursive Backtracking
Decision Tree Evaluation
Optimize Performance
Game State Management

Link: Play

The Mini-Max algorithm is a fundamental decision-making technique used in game theory and artificial intelligence. We present the implementation of the Mini-Max algorithm in a general context to showcase its functionality and its application to the classic game of Tic-Tac-Toe. We aim to determine the best outcome that the maximizer or minimizer can achieve in both scenarios. Through this work, we demonstrate the versatility and effectiveness of the Mini-Max algorithm in various two-player zero-sum games

HTML-CSS-JavaScript
Python (For code implementation)    

Member 1: Harshvardhan Mehta
Member 1: Harshitha JS
Member 1: Isha Gupta

1. Alpha-Beta Pruning: Enhance the algorithm's efficiency by pruning unnecessary nodes, making it faster.
2. Heuristic Evaluation: Implement heuristic functions to estimate game state values, allowing for scalability to more complex games.
3. Adaptation to Other Games: Extend the algorithm to more complex games like Chess or Connect Four, dealing with larger state spaces.
4. Machine Learning Integration: Combine with machine learning for more adaptive AI, using techniques like reinforcement learning.
5. User Interface Enhancements: Improve the game's UI, add difficulty levels, multiplayer options, and detailed move explanations.
6. Educational Tools: Develop interactive tutorials and visualizations to teach algorithms, game theory, and AI concepts.