  Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs
used to model situations where the agent's initial belief or observation
probabilities are imprecise due to lack of past experiences or insufficient
data collection. However, like probabilistic POMDPs, optimally solving
pi-POMDPs is intractable: the finite belief state space exponentially grows
with the number of system's states. In this paper, a possibilistic version of
Mixed-Observable MDPs is presented to get around this issue: the complexity of
solving pi-POMDPs, some state variables of which are fully observable, can be
then dramatically reduced. A value iteration algorithm for this new formulation
under infinite horizon is next proposed and the optimality of the returned
policy (for a specified criterion) is shown assuming the existence of a "stay"
action in some goal states. Experimental work finally shows that this
possibilistic model outperforms probabilistic POMDPs commonly used in robotics,
for a target recognition problem where the agent's observations are imprecise.
