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About this paper
Boning Li, Longbo Huang
The Abstraction Agent introduces an LLM-driven pipeline for constructing game information abstractions solely from natural-language rules, bypassing the need for domain-specific evaluators or game-tree traversals. This approach reduced exploitability by up to 62% in HUNL turn endgames compared to an EHS baseline and demonstrated effective portability across diverse imperfect-information games.
Key insights
- Advanced LLMs can infer and articulate strategically relevant features (e.g., "Nut_Ceiling", "Blocker_Value") from natural-language game rules, even for novel games not present in their pretraining data.
- Decomposing the problem, where LLMs handle qualitative feature elicitation and traditional algorithms handle quantitative equilibrium computation, overcomes common LLM limitations in game-playing.
- Multi-dimensional abstractions derived from LLM-generated features yield higher quality game-solving performance than single-dimensional scalar metrics, especially at finer granularities.
Summary by alphaXiv.
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