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Research questionHow can imperfect-information game solvers build useful state abstractions without domain-specific evaluators?Game-solving algorithms must group strategically similar private states into a manageable number of buckets. These groupings typically depend on hand-crafted evaluators that require substantial domain knowledge and engineering effort.
AI
Machine Learning
Multi-agent Systems
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Technology
Latest papersRecent research connected to this question, newest first.Abstraction AgentThe source examines a zero-shot LLM pipeline that derives strategic features from natural-language game descriptions and clusters private states without game-specific evaluators, training data, or game-tree traversal during abstraction construction. Evidence covers heads-up no-limit Texas hold’em, ROVER Trials, four-card Pot-Limit Omaha, and Riichi Mahjong; reported quantitative results focus on Texas hold’em and ROVER Trials.research paper · Sep 3, 2026
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