Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes
复制标题
部分可观测马尔可夫决策过程中基于点的模型检验方法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Mykel J. Kochenderfer
中科院分区：
文献类型：
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作者：
Maxime Bouton;Jana Tumova;Mykel J. Kochenderfer
自治系统通常需要在部分可观察的环境中运行。它们必须可靠地执行指定的目标，即使有关环境状态的信息不完整。我们提出了一种方法来合成的政策，满足线性时序逻辑公式的部分可观察马尔可夫决策过程（POMDP）。通过制定规划问题，我们展示了如何使用基于点的值迭代方法来有效地近似满足所需逻辑公式的最大概率，并计算相关的信念状态策略。我们证明了我们的方法可扩展到大POMDP域，并提供了强大的界限所产生的政策的性能。
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.
影响因子：
1.3
作者：
Norman, Gethin;Parker, David;Zou, Xueyi
通讯作者：
Zou, Xueyi