论文标题
基于点的模型检查方法，以部分可观察到的马尔可夫决策过程
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes
论文作者
论文摘要
通常需要自主系统在部分可观察到的环境中操作。即使有关环境状态的不完整信息，他们也必须可靠地执行指定的目标。我们提出了一种方法，以合成在可观察到的马尔可夫决策过程（POMDP）中满足线性时间逻辑公式的策略。通过制定计划问题，我们展示了如何使用基于点的价值迭代方法有效近似满足所需逻辑公式并计算相关信仰状态策略的最大可能性。我们证明了我们的方法扩展到大型POMDP域，并为产生策略的性能提供了强大的界限。
Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). By formulating a planning problem, we show how to use point-based value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula and compute the associated belief state policy. We demonstrate that our method scales to large POMDP domains and provides strong bounds on the performance of the resulting policy.
