Mini Chess AI Battle Platform: Intelligent Gameplay with Minimax Algorithm and Alpha-Beta Pruning

This article introduces a web-based mini chess AI game project that uses the classic Minimax algorithm and Alpha-Beta pruning technology to implement AI battles, combined with modern responsive interface design. The project demonstrates the practical application of classic game theory algorithms in board games and has high educational value, suitable for AI learners to understand search algorithms and game theory. The original author of the project is sonimochleviansyah, open-sourced on GitHub, link: https://github.com/sonimochleviansyah/mini-chess-ai, release date: 2026-06-01.

Board games are important testbeds for AI research; the evolution from Deep Blue to AlphaGo has witnessed algorithmic progress. Minimax and Alpha-Beta pruning are classic game tree search methods and remain core content in AI courses. Mini chess is a simplified variant of international chess (e.g., smaller board, fewer pieces), retaining strategic depth while reducing complexity, making it suitable for algorithm demonstrations and human-AI battle experiments.

The Minimax algorithm is the basic search algorithm for two-player zero-sum games, based on the idea of 'minimizing maximum loss'. The algorithm constructs a game tree, alternately selecting the maximum value for the Max player (AI) and the minimum value for the Min player (opponent). The evaluation function is the core, quantifying the board state with factors including piece value, positional advantage, king safety, pawn structure, etc., which directly affects the AI's playing style and strength.

The original Minimax algorithm's computational complexity grows exponentially with depth. Alpha-Beta pruning skips irrelevant branches by maintaining Alpha (the minimum score the Max player can guarantee) and Beta (the maximum score the Min player can accept), significantly reducing the number of search nodes. In the best case, the complexity drops from O(b^d) to O(b^(d/2)). Move ordering (e.g., MVV-LVA, killer heuristic) can improve pruning efficiency, and iterative deepening search combined with time control optimizes performance.

The project uses a front-end and back-end separated web architecture. The front-end uses modern JS frameworks to build a responsive board interface; the AI engine implements board representation (bitboard technology), move generation, search algorithms, and evaluation functions. The move generator enumerates legal moves and filters illegal ones (e.g., moves that leave the player's own king in check). Real-time communication is achieved via WebSocket/HTTP API, supporting AI thinking visualization and animation effects.

The UI uses glowing effects and a dark theme; the board features classic black and white squares, and pieces use clear icons/Unicode. Interactive functions include click-to-select pieces, drag-to-move, undo moves, difficulty adjustment (AI search depth), game records (algebraic notation), and a timer. The responsive layout adapts to multiple devices and supports theme customization.

The project covers core knowledge points such as recursion, search algorithms, and game theory, exercising programming and problem abstraction skills. Visualization helps understand algorithms (game tree expansion, pruning process). Expansion directions include transposition tables, opening books, endgame databases; Monte Carlo Tree Search (MCTS); deep learning combined with neural networks (e.g., AlphaZero); multi-threaded/distributed search, etc.

The mini chess AI project demonstrates the elegant implementation of classic algorithms in a modern web environment. Although Minimax and Alpha-Beta pruning have been around for a long time, they still have strong vitality. For AI learners, implementing such a project is an excellent way to understand search algorithms and game theory. In the current era dominated by deep learning, reviewing classic methods helps build a complete knowledge system and understand the evolution of technology.