  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.
