Efficient Policies for Stationary Possibilistic Markov Decision Processes

Possibilistic Markov Decision Processes offer a compact and tractable way to represent and solve problems of sequential decision under qualitative uncertainty. Even though appealing for its ability to handle qualitative problems, this model suffers from the drowning effect that is inherent to possibilistic decision theory. The present paper proposes to escape the drowning effect by extending to stationary possibilistic MDPs the lexicographic preference relations defined in [6] for nonsequential decision problems and provides a value iteration algorithm to compute policies that are optimal for these new criteria.