arXiv:2512.10279v3 Announce Type: replace-cross Abstract: We present an algorithm for computing evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games of imperfect information. Our main algorithm is for two-player games, and we describe how it can be extended to multiplayer games. The algorithm is sound and computes all ESSs in nondegenerate games and a subset of them in degenerate games which contain an infinite continuum of symmetric Nash equilibria. The algorithm is anytime and can be stopped early to find one or more ESSs. We experiment on an imperfect-inf
The paper presents a concrete algorithm for computing evolutionarily stable strategies in imperfect-information games, indicating a technical breakthrough in game theory applications for AI.
This development could enhance the 'reasoning' capabilities of advanced AI systems, particularly in multi-agent environments with incomplete information, fostering more robust and strategic AI behaviors.
The ability to compute ESSs more effectively means AI agents can be designed to act with greater strategic stability and predictability in complex, competitive scenarios.
- · AI agents developers
- · Game theory researchers
- · Defence tech sector
- · Strategic planning software
- · Adversarial AI developers lacking ESS integration
More sophisticated and robust AI agents capable of operating in imperfect-information environments.
Accelerated development of AI systems for complex strategic domains like cybersecurity, military simulation, and automated negotiation.
Potential for AI agents to achieve superior strategic outcomes against human or less-advanced AI players in competitive fields.
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Read at arXiv cs.AI