A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments

In this article we propose a qualitative ( ordi­ nal) counterpart for the Partially Observable Markov Decision Processes rnodel (POMDP) in which the uncertainty, as well as the prefer­ ences of the agent, are modeled by possibility distributions. This qualitative counterpart of the POMDP model relies on a possibilistic theory of decision under uncertainty, recently developed. One advantage of such a qualitative frame­ work is its ability to escape from the classi­ cal obstacle of stochastic POMDPs, in which even with a finite state space, the obtained belief state space of the POMDP is infinite. Instead, in the possibilistic framework even if exponentially larger than the state space, the belief state space remains finite.