The verification problem in MDPs asks whether, for any policy resolving the nondeterminism, the probability that something bad happens is bounded by some given threshold. This verification problem is often overly pessimistic, as the policies it considers may depend on the complete system state. This paper considers the verification problem for partially observable MDPs, in which the policies make their decisions based on (the history of) the observations emitted by the system. We present an abstraction-refinement framework extending previous instantiations of the Lovejoy-approach. Our experiments show that this framework significantly improves the scalability of the approach.

中文翻译：

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无限期 POMDP 的验证

MDP 中的验证问题询问，对于解决不确定性的任何策略，发生坏事的概率是否受某个给定阈值的限制。这个验证问题通常过于悲观，因为它考虑的策略可能取决于完整的系统状态。本文考虑了部分可观察 MDP 的验证问题，其中策略根据系统发出的观察（历史）做出决策。我们提出了一个抽象细化框架，扩展了以前的 Lovejoy 方法实例。我们的实验表明，该框架显着提高了该方法的可扩展性。