Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives
Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives
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DOI：
10.48550/arxiv.2304.11641
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Lening Li;Hazhar Rahmani;Jie Fu
中科院分区：
文献类型：
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作者：
Lening Li;Hazhar Rahmani;Jie Fu
本文研究了概率环境中的时间规划，建模为标记马尔可夫决策过程（MDP），用户偏好的多个时间目标。现有的作品反映了这样的偏好，作为一个优先的目标清单。本文介绍了一种新的规范语言，称为有限迹上的优先定性选择线性时序逻辑，它是在优先定性选择逻辑的基础上，增加了优先合取和有序析取，从而扩充了有限迹上的线性时序逻辑。这种语言允许简洁地指定时间目标与相应的偏好完成每个时间任务。描述系统行为的有限迹线基于它们相对于公式的不满意分数进行排名。我们提出了一个系统的翻译，从新的语言加权确定性有限自动机。利用这个计算模型，我们制定并解决了一个问题，计算最佳的政策，最大限度地减少预期分数的不满意用户的喜好。我们证明了有效性和适用性的逻辑和算法的几个案例研究，每个详细的分析。
This paper studies temporal planning in probabilistic environments, modeled as labeled Markov decision processes (MDPs), with user preferences over multiple temporal goals. Existing works reflect such preferences as a prioritized list of goals. This paper introduces a new specification language, termed prioritized qualitative choice linear temporal logic on finite traces, which augments linear temporal logic on finite traces with prioritized conjunction and ordered disjunction from prioritized qualitative choice logic. This language allows for succinctly specifying temporal objectives with corresponding preferences accomplishing each temporal task. The finite traces that describe the system's behaviors are ranked based on their dissatisfaction scores with respect to the formula. We propose a systematic translation from the new language to a weighted deterministic finite automaton. Utilizing this computational model, we formulate and solve a problem of computing an optimal policy that minimizes the expected score of dissatisfaction given user preferences. We demonstrate the efficacy and applicability of the logic and the algorithm on several case studies with detailed analyses for each.