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

TL;DR

The paper tackles the difficult problem of predicting human chess moves by incorporating domain-expert knowledge into a behavioral programming framework. It introduces BP-based modeling of opening-phase strategies, constructs a dataset of BP-state transitions from lichess openings, and trains standard ML models on this knowledge-infused representation. Compared with Maia Chess, a large neural baseline, the BP-driven approach achieves substantial gains in binary move-prediction accuracy, while regression gains are more modest but still informative; the method also offers greater interpretability and lower computational requirements. The work demonstrates that explicit knowledge representation can enhance human-behavior prediction in complex domains, with implications for AI explainability and efficiency in human-centered decision tasks.

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.

Figures (2)

• Figure 1: Binary Classification Performance
• Figure 2: Regression Mean Error Performance