A Behavior-Based Knowledge Representation Improves Prediction of Players' Moves in Chess by 25%

2025-04-07

AI Summary

Predicting opening moves for intermediate-level human chess players remains challenging, as traditional engines rely on the assumption of optimal play and thus fail to capture individual strategic styles and bounded-rational decision preferences. Method: This paper proposes a behavior-driven knowledge representation framework that explicitly encodes player-specific stylistic features—integrating expert-guided feature engineering, behavioral pattern mining, and style vectorization—and trains a supervised learning model. Crucially, it abandons the rationality assumption inherent in game-theoretic modeling. Contribution/Results: Evaluated on real-game data from intermediate players, our approach achieves a 25% improvement in move prediction accuracy over baseline methods. It significantly outperforms zero-shot transfer performances of AlphaZero and Stockfish, demonstrating superior generalization to human-like play. By grounding chess AI modeling in empirical human behavior rather than idealized rationality, this work establishes a novel paradigm for human-centered chess intelligence.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Predicting player behavior in strategic games, especially complex ones like chess, presents a significant challenge. The difficulty arises from several factors. First, the sheer number of potential outcomes stemming from even a single position, starting from the initial setup, makes forecasting a player's next move incredibly complex. Second, and perhaps even more challenging, is the inherent unpredictability of human behavior. Unlike the optimized play of engines, humans introduce a layer of variability due to differing playing styles and decision-making processes. Each player approaches the game with a unique blend of strategic thinking, tactical awareness, and psychological tendencies, leading to diverse and often unexpected actions. This stylistic variation, combined with the capacity for creativity and even irrational moves, makes predicting human play difficult. Chess, a longstanding benchmark of artificial intelligence research, has seen significant advancements in tools and automation. Engines like Deep Blue, AlphaZero, and Stockfish can defeat even the most skilled human players. However, despite their exceptional ability to outplay top-level grandmasters, predicting the moves of non-grandmaster players, who comprise most of the global chess community -- remains complicated for these engines. This paper proposes a novel approach combining expert knowledge with machine learning techniques to predict human players' next moves. By applying feature engineering grounded in domain expertise, we seek to uncover the patterns in the moves of intermediate-level chess players, particularly during the opening phase of the game. Our methodology offers a promising framework for anticipating human behavior, advancing both the fields of AI and human-computer interaction.

Problem

• Predicting human chess moves despite complexity and unpredictability
• Improving move prediction for non-grandmaster players using AI
• Combining expert knowledge and ML to analyze opening patterns

Innovation

• Behavior-based knowledge representation for chess
• Combining expert knowledge with machine learning
• Feature engineering for intermediate player patterns