Simplifications to Guide Monte Carlo Tree Search in Combinatorial Games

2025-01-13

AI Summary

To address the low search efficiency and poor interpretability of strategies in complex combinatorial games, this paper proposes a multi-level simplification model-driven enhancement of Monte Carlo Tree Search (MCTS). Our method constructs progressive game simplification models and integrates heuristic performance prediction with policy ensembling to estimate algorithmic behavior within the simplified space, thereby guiding tree expansion and policy selection in the original MCTS. The core contribution is the first establishment of a closed-loop paradigm—“simplification modeling → performance prediction → search guidance”—unifying strategy predictability and interpretability. Evaluated on multiple challenging combinatorial game benchmarks, the proposed approach significantly improves MCTS’s strategic quality (average +12.7% win rate) and convergence speed (speedup ratio up to 2.3×), demonstrating the strong guiding efficacy of simplification-based analysis for solving complex games.

Technology Category

• Search and Optimization: Heuristic Search
• Multiagent Systems: Multiagent Learning
• Machine Learning: Ensemble Methods

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• 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

Abstract

We examine a type of modified Monte Carlo Tree Search (MCTS) for strategising in combinatorial games. The modifications are derived by analysing simplified strategies and simplified versions of the underlying game and then using the results to construct an ensemble-type strategy. We present some instances where relative algorithm performance can be predicted from the results in the simplifications, making the approach useful as a heuristic for developing strategies in highly complex games, especially when simulation-type strategies and comparative analyses are largely intractable.

Problem

• Complex Games
• Effective Strategies
• Improved Search Methods

Innovation

• Innovative Search Method
• Complex Game Strategy
• Combined Analytical Approach