This work studies the problem of ad hoc teamwork in teams composed of agents with differing computational capabilities. We consider cooperative multi-player games in which each agent's policy is constrained by a private capability parameter, and agents with higher capabilities are able to simulate the behavior of agents with lower capabilities (but not vice-versa). To address this challenge, we propose an algorithm that maintains a belief over the other agents' capabilities and incorporates this belief into the planning process. Our primary innovation is a novel framework based on capability type structures, which ensures that the belief updates remain consistent and informative without constructing the infinite hierarchy of beliefs. We also extend our techniques to settings where the agents' observations are subject to noise. We provide examples of games in which deviations in capability between oblivious agents can lead to arbitrarily poor outcomes, and experimentally validate that our capability-aware algorithm avoids the anti-cooperative behavior of the naive approach in these toy settings as well as a more complex cooperative checkers environment.

翻译：本文研究了由具有不同计算能力的智能体组成的团队中的临时团队合作问题。我们考虑协作型多人游戏，其中每个智能体的策略受限于一个私有能力参数，且能力更高的智能体能够模拟能力较低的智能体的行为（反之则不能）。为应对这一挑战，我们提出了一种算法，该算法维护关于其他智能体能力的信念，并将此信念融入规划过程中。我们的主要创新在于提出了一种基于能力类型结构的新框架，该框架确保信念更新保持一致且富有信息性，而无需构建无限的信念层级。我们还将所提出的技术扩展至智能体观测存在噪声的场景。我们给出了若干游戏实例，在这些实例中，无感知智能体之间的能力偏差可能导致任意糟糕的结果，并通过实验验证了在玩具场景以及更复杂的协作跳棋环境中，我们提出的能力感知算法能够避免朴素方法中出现的反协作行为。