Abstraction Agent
Information abstraction, which groups strategically similar private states into a tractable number of buckets, is essential for scaling game-solving algorithms to large imperfect-information games. Constructing effective abstractions, however, has traditionally required domain-specific evaluators such as hand-strength calculators or equity estimators, which demand expert knowledge and engineering effort and are unavailable for most less-studied games. We propose the Abstraction Agent, a zero-shot pipeline that uses a large language model (LLM) to discover continuous strategic features from a natural-language game description, score private states on these features, and cluster them into a...
| Authors | Boning Li, Longbo Huang |
|---|---|
| Primary category | cs.MA |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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