Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations

Craig Boutilier, David Poole

Partially-observable Markov decision processes provide a general model for decision theoretic planning problems, allowing trade-offs between various courses of actions to be determined under conditions of uncertainty, and incorporating partial observations made by an agent. Dynamic programming algorithms based on the belief state of an agent can be used to construct optimal policies without explicit consideration of past history, but at high computational cost. In this paper, we discuss how structured representations of system dynamics can be incorporated in classic POMDP solution algorithms. We use Bayesian networks with structured conditional probability matrices to represent POMDPs, and use this model to structure the belief space for POMDP algorithms, allowing irrelevant distinctions to be ignored. Apart from speeding up optimal policy construction, we suggest that such representations can be exploited in the development of useful approximation methods.


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