A Possibilistic Model for Qualitative Sequential Decision Problems under
  Uncertainty in Partially Observable Environments
  In this article we propose a qualitative (ordinal) counterpart for the
Partially Observable Markov Decision Processes model (POMDP) in which the
uncertainty, as well as the preferences 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 framework is its ability to
escape from the classical 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.
