Neural Network Chess AI Self-Training via Monte Carlo Tree Search
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Solution Overview
Problem
Existing chess training methods, particularly those based on expert systems, rely heavily on human data and experience, limiting their ability to adapt and improve beyond human capabilities, especially in complex games like Go, where decision-making space is vast.
Innovation Solution
A chess self-learning method and device utilizing machine learning, specifically a neural network and Monte Carlo tree search, that generates game data through self-play and trains without human input, using a neural network as a move selector and value evaluator, and a Monte Carlo tree as a strategy optimizer to improve AI chess abilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert system is used for chess training, then stability and response speed are improved, but adaptability and ability to surpass human performance deteriorate
Solution Approach 1:
The system enables the AI to self-play and self-train without human intervention. The neural network plays games against itself, generates training data from these self-play games, and continuously improves its own performance through self-generated training data, eliminating dependence on human expertise while maintaining system stability
Solution Approach 2:
The system dynamically adjusts training parameters including learning rate, network depth, and training iterations based on performance metrics. The neural network's weights and biases are continuously optimized through gradient descent, allowing the system to adapt its learning process and surpass human performance levels
2Speed
If expert system is used for chess training, then response speed is improved, but ability to handle complex decision-making spaces deteriorates
Solution Approach 1:
The system replaces traditional expert system rule-based mechanics with neural network-based machine learning. The neural network processes board states and generates moves through learned patterns rather than predefined rules, enabling superior handling of complex decision-making spaces in games like Go while maintaining fast response speeds through optimized inference
3Measurement precision
If supervised learning is used for AI training, then training level is improved, but data requirements and training complexity increase
Solution Approach 1:
The system generates its own training data through self-play games. The neural network plays against itself, and the outcomes of these games automatically become training examples. This self-generated data eliminates the need for large external datasets while achieving high training levels through continuous self-improvement
Solution Approach 2:
The training process is dynamic and adaptive. The system continuously generates new training data as the neural network improves, ensuring the training data evolves with the model's capabilities. This dynamic data generation approach maintains high training effectiveness without requiring static large-scale datasets
Data Source
AI summary
The present disclosure discloses a chess self-learning method and device based on machine learning, a move selection output layer and a value evaluation output layer of the method share the same input layer and hidden layer of a neural network, and a Monte Carlo tree search tree is used to construct a strategy optimizer. The training process of the method is divided into two parts, namely data generation and neural network training, so that an error between a value scalar outputted by a neural network and a final result of self-play is as small as possible, and a move vector outputted by the neural network is as close as possible to a decision vector given by a Monte Carlo tree for each search step. The present disclosure aims to construct an Al chess player for people to play chess.