Abstract
This paper proposes a new model, the EMDP (Evidential Markov Decision Process). It is a MDP (Markov Decision Process) for belief functions in which rewards are defined for each state transition, like in a classical MDP, whereas the transitions are modeled as in an EMC (Evidential Markov Chain), i.e. they are sets transitions instead of states transitions. The EMDP can fit to more applications than a MDPST (MDP with Set-valued Transitions). Generalizing to belief functions allows us to cope with applications with high uncertainty (imprecise or lacking data) where probabilistic approaches fail. Implementation results are shown on a search-and-rescue unmanned rotorcraft benchmark.
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