We consider the problem of synthesizing resilient and stochastically stable strategies for systems of cooperating agents striving to minimize the expected time between consecutive visits to selected locations in a known environment. A strategy profile is resilient if it retains its functionality even if some of the agents fail, and stochastically stable if the visiting time variance is small. We design a novel specification language for objectives involving resilience and stochastic stability, and we show how to efficiently compute strategy profiles (for both autonomous and coordinated agents) optimizing these objectives. Our experiments show that our strategy synthesis algorithm can construct highly non-trivial and efficient strategy profiles for environments with general topology.

翻译：我们考虑在已知环境中合作智能体系统综合弹性且随机稳定策略的问题，旨在最小化连续两次访问特定位置之间的期望时间。当存在智能体失效时，若策略仍能保持其功能，则称该策略概型具有弹性；若访问时间方差较小，则称其具有随机稳定性。我们设计了一种新颖的规范语言用于描述涉及弹性与随机稳定性的目标，并展示了如何高效计算优化这些目标的策略概型（适用于自主智能体与协作智能体）。实验表明，我们的策略综合算法能够为一般拓扑环境构建高度非平凡且高效的策略概型。