Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives
This work addresses temporal planning in probabilistic environments for users needing prioritized preferences, representing an incremental advancement by extending existing logic frameworks.
The paper tackles the problem of probabilistic planning with user preferences over multiple temporal logic objectives by introducing a new specification language, prioritized qualitative choice linear temporal logic on finite traces, and proposes an algorithm to compute optimal policies that minimize expected dissatisfaction scores, demonstrating efficacy through case studies.
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.