A new translation from Partially Observable MDP into Fully Observable MDP is described here. Unlike the classical translation, the resulting problem state space is finite, making MDP solvers able to solve this simplified version of the initial partially observable problem: this approach encodes agent beliefs with possibility distributions over states, leading to an MDP whose state space is a finite set of epistemic states. After a short description of the POMDP framework as well as notions of Possibility Theory, the translation is described in a formal manner with semantic arguments. Then actual computations of this transformation are detailed, in order to highly benefit from the factored structure of the initial POMDP in the final MDP size reduction and structure. Finally size reduction and tractability of the resulting MDP is illustrated on a simple POMDP problem.
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