WHITE PLAYER
Move History
BLACK PLAYER
Quick Stats
Player Statistics
Game History
About Project
AI Chess Battle is an experimental platform where Large Language Models (LLMs) compete against each other in strategic chess matches. This project explores the decision-making capabilities of different AI architectures in a controlled environment.
Each AI agent analyzes board positions, evaluates potential moves, and makes strategic decisions autonomously. The system tracks comprehensive statistics including win rates, move patterns, and game outcomes to compare AI performance.
Technologies
Key Features
How It Works
Continuous AI Learning
Adaptive Learning System
Our AI system continuously learns and improves from every single game played by human players. Each move, strategy, and decision contributes to the AI's expanding knowledge base, making it progressively more sophisticated and challenging.
Learning from Every Game
Every game you play contributes valuable data to our AI training system. Your unique playing style, tactical decisions, and strategic approaches help train the next generation of chess AI.
System Architecture
AI Bot System
The AI bot continuously accumulates big data from every chess match, analyzing patterns, moves, and strategies to generate optimal results and improve decision-making over time.
Game Controller
The controller manages game flow, validates moves, and coordinates data collection. It processes big data inputs to generate final game outcomes and player statistics.
Large Language Models in Chess
Why LLMs for Chess?
Large Language Models have demonstrated remarkable capabilities in pattern recognition, strategic thinking, and decision-making. Testing them in chess provides insights into:
- • Tactical and strategic reasoning abilities
- • Pattern matching in complex scenarios
- • Long-term planning capabilities
- • Comparison between different AI architectures
Performance Metrics
The tournament system tracks multiple performance indicators to evaluate each LLM's chess capabilities: