Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess

• URL: http://arxiv.org/abs/2507.21488v1
• Date: Tue, 29 Jul 2025 04:09:31 GMT
• Title: Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess
• Authors: Zhenwei Tang, Difan Jiao, Eric Xue, Reid McIlroy-Young, Jon Kleinberg, Siddhartha Sen, Ashton Anderson,
• Abstract summary: Maia4All is a framework designed to learn and adapt to individual decision-making styles efficiently.<n>Maia4All achieves individual human behavior modeling in chess with only 20 games, compared to the 5,000 games required previously.
• Score: 10.090379544417432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As humans seek to collaborate with, learn from, and better understand artificial intelligence systems, developing AIs that can accurately emulate individual decision-making becomes increasingly important. Chess, a long-standing AI benchmark with precise skill measurement, offers an ideal testbed for human-AI alignment. However, existing approaches to modeling human behavior require prohibitively large amounts of data from each individual, making them impractical for new or sparsely represented users. In this work, we introduce Maia4All, a framework designed to learn and adapt to individual decision-making styles efficiently, even with limited data. Maia4All achieves this through a two-stage optimization process: (1) an enrichment step, which bridges population and individual-level human behavior modeling with a prototype-enriched model, and (2) a democratization step, which leverages ability levels or user prototypes to initialize and refine individual embeddings with minimal data. Our experimental results show that Maia4All can accurately predict individual moves and profile behavioral patterns with high fidelity, establishing a new standard for personalized human-like AI behavior modeling in chess. Maia4All achieves individual human behavior modeling in chess with only 20 games, compared to the 5,000 games required previously, representing a significant improvement in data efficiency. Our work provides an example of how population AI systems can flexibly adapt to individual users using a prototype-enriched model as a bridge. This approach extends beyond chess, as shown in our case study on idiosyncratic LLMs, highlighting its potential for broader applications in personalized AI adaptation.

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