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Showing 1 to 2 of 2 for “"Partially Observable Markov Decision Processes (POMDP)"”.
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Belief change in probabilistic knowledge representations for open and dynamic computing environments
… structure at any given time is unknown and is unobservable. Only the data emitted from the domain is observable. Further to the foregoing, the Belief Change Model needs to cater to both changes necessititated by the correction of incorrect Beliefs (Belief Revision) and changes necessitated by …
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Optimization for stochastic, partially observed systems using a sampling-based approach to learn switched policies
… a new method for learning policies for large, partially observable Markov decision processes (POMDPs) that require long time horizons for planning. Computing optimal policies for POMDPs is an intractable problem and, in practice, dimensionality renders exact solutions essentially unreachable …