Imperfect-Information Games
Overview
These games model settings such as poker, negotiation, and pursuit-evasion, where players must act using incomplete observations and beliefs about hidden cards, types, or intentions. Researchers develop equilibrium concepts and solvers such as counterfactual regret minimization, alongside Bayesian and Stackelberg methods for reasoning about beliefs, commitments, and incentives. Reinforcement learning, search, and opponent modeling produce agents for large games, while benchmarks and variance-reduced evaluation help compare their strength and robustness. A central challenge is scaling methods without losing strategic guarantees, especially when agents must adapt to opponents, exploit mistakes, or remain safe under uncertainty.
Latest papers
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