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This paper studies temporal planning in probabilistic environments, modeledas labeled Markov decision processes (MDPs), with user preferences overmultiple temporal goals. Existing works reflect such preferences as aprioritized 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 prioritizedconjunction and ordered disjunction from prioritized qualitative choice logic.This language allows for succinctly specifying temporal objectives withcorresponding preferences accomplishing each temporal task. The finite tracesthat describe the system's behaviors are ranked based on their dissatisfactionscores with respect to the formula. We propose a systematic translation fromthe new language to a weighted deterministic finite automaton. Utilizing thiscomputational model, we formulate and solve a problem of computing an optimalpolicy that minimizes the expected score of dissatisfaction given userpreferences. We demonstrate the efficacy and applicability of the logic and thealgorithm on several case studies with detailed analyses for each.

Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives