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[2602.15252] Decision Making under Imperfect Recall: Algorithms and Benchmarks
Summary
This paper presents a benchmark suite for decision-making under imperfect recall in game theory, introducing regret matching algorithms that outperform traditional optimizers in constrained optimization tasks.
Why It Matters
Understanding decision-making under imperfect recall is crucial for developing AI systems that can handle incomplete information. This research provides benchmarks that can enhance AI safety and privacy, making it relevant for both academic and practical applications in AI development.
Key Takeaways
- Introduces the first benchmark suite for imperfect-recall decision problems.
- Regret matching algorithms significantly outperform traditional optimizers.
- Addresses important issues in AI safety and privacy through benchmark evaluations.
- Demonstrates the applicability of regret matching in large-scale optimization.
- Provides a foundation for future research in game theory and AI decision-making.
Computer Science > Computer Science and Game Theory arXiv:2602.15252 (cs) [Submitted on 16 Feb 2026] Title:Decision Making under Imperfect Recall: Algorithms and Benchmarks Authors:Emanuel Tewolde, Brian Hu Zhang, Ioannis Anagnostides, Tuomas Sandholm, Vincent Conitzer View a PDF of the paper titled Decision Making under Imperfect Recall: Algorithms and Benchmarks, by Emanuel Tewolde and 4 other authors View PDF Abstract:In game theory, imperfect-recall decision problems model situations in which an agent forgets information it held before. They encompass games such as the ``absentminded driver'' and team games with limited communication. In this paper, we introduce the first benchmark suite for imperfect-recall decision problems. Our benchmarks capture a variety of problem types, including ones concerning privacy in AI systems that elicit sensitive information, and AI safety via testing of agents in simulation. Across 61 problem instances generated using this suite, we evaluate the performance of different algorithms for finding first-order optimal strategies in such problems. In particular, we introduce the family of regret matching (RM) algorithms for nonlinear constrained optimization. This class of parameter-free algorithms has enjoyed tremendous success in solving large two-player zero-sum games, but, surprisingly, they were hitherto relatively unexplored beyond that setting. Our key finding is that RM algorithms consistently outperform commonly employed first-order ...
