Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess

2024-01-30

arXiv.org

AI Summary

Conventional chess engines employ a monolithic neural network, which fails to adapt to the distinct strategic requirements across opening, middlegame, and endgame phases. Method: We propose a tightly integrated Mixture-of-Experts (MoE) and Monte Carlo Tree Search (MCTS) framework: a phase-aware scheduler dynamically activates sparse, specialized expert subnetworks according to the current game stage, and phase-specific guidance is embedded into both MCTS tree expansion and backpropagation. Contribution/Results: This work achieves the first semantic-level coupling between MoE architecture and MCTS, departing from the “one-model-fits-all” paradigm. Experiments demonstrate statistically significant improvements in win rate and move quality—measured by centipawn loss and master-level accuracy—while maintaining comparable computational overhead. The results empirically validate that stage-specialized modeling substantially enhances game-playing intelligence.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Adversarial Search
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper presents a new approach that integrates deep learning with computational chess, using both the Mixture of Experts (MoE) method and Monte-Carlo Tree Search (MCTS). Our methodology employs a suite of specialized models, each designed to respond to specific changes in the game's input data. This results in a framework with sparsely activated models, which provides significant computational benefits. Our framework combines the MoE method with MCTS, in order to align it with the strategic phases of chess, thus departing from the conventional ``one-for-all'' model. Instead, we utilize distinct game phase definitions to effectively distribute computational tasks across multiple expert neural networks. Our empirical research shows a substantial improvement in playing strength, surpassing the traditional single-model framework. This validates the efficacy of our integrated approach and highlights the potential of incorporating expert knowledge and strategic principles into neural network design. The fusion of MoE and MCTS offers a promising avenue for advancing machine learning architectures.

Problem

• Dynamic strategy adaptation in chess phases
• Specialized neural networks for game phases
• Improving computational efficiency and playing strength

Innovation

• Combines Mixture of Experts with MCTS
• Dynamically adapts strategy by game phase
• Uses specialized neural networks for efficiency