论文标题
在随机系统中具有比时间逻辑目标偏好的随机系统的机会主义定性计划
Opportunistic Qualitative Planning in Stochastic Systems with Preferences over Temporal Logic Objectives
论文作者
论文摘要
偏好在确定哪些目标/约束可以同时满足时要满足哪些目标/约束起着关键作用。在这项工作中，我们在以马尔可夫决策过程为模型的随机系统中研究基于偏好的计划，但要偏爱与时间扩展目标的不完全偏好。我们的贡献是三个方面：首先，我们引入了一种偏好语言，以指定比时间扩展目标的偏好。其次，我们定义了一种新型的自动机理论模型，以表示由给定偏好关系引起的预订。偏好的自动机表示使我们能够为随机系统开发基于偏好的计划算法。最后，我们展示了如何合成在随机系统中以积极的概率或概率的结果来改善当前可满足结果的结果的机会主义策略。我们使用机器人运动计划示例说明了解决方案方法。
Preferences play a key role in determining what goals/constraints to satisfy when not all constraints can be satisfied simultaneously. In this work, we study preference-based planning in a stochastic system modeled as a Markov decision process, subject to a possible incomplete preference over temporally extended goals. Our contributions are three folds: First, we introduce a preference language to specify preferences over temporally extended goals. Second, we define a novel automata-theoretic model to represent the preorder induced by given preference relation. The automata representation of preferences enables us to develop a preference-based planning algorithm for stochastic systems. Finally, we show how to synthesize opportunistic strategies that achieves an outcome that improves upon the current satisfiable outcome, with positive probability or with probability one, in a stochastic system. We illustrate our solution approaches using a robot motion planning example.
