  We present a formal language for specifying qualitative preferences over
temporal goals and a preference-based planning method in stochastic systems.
Using automata-theoretic modeling, the proposed specification allows us to
express preferences over different sets of outcomes, where each outcome
describes a set of temporal sequences of subgoals. We define the value of
preference satisfaction given a stochastic process over possible outcomes and
develop an algorithm for time-constrained probabilistic planning in labeled
Markov decision processes where an agent aims to maximally satisfy its
preference formula within a pre-defined finite time duration. We present
experimental results using a stochastic gridworld example and discuss possible
extensions of the proposed preference model.
