Probabilistic Planning with Preferences over Temporal Goals
This work addresses planning under uncertainty with user preferences, which is incremental as it builds on existing automata-theoretic and MDP frameworks.
The authors tackled the problem of probabilistic planning with qualitative preferences over temporal goals by developing a formal language and a preference-based planning method for stochastic systems, achieving an algorithm for time-constrained planning in labeled Markov decision processes.
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.