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Partially observable Markov decision processes (POMDPs) provide an elegantmathematical framework for modeling complex decision and planning problems instochastic domains in which states of the system are observable onlyindirectly, via a set of imperfect or noisy observations. The modelingadvantage of POMDPs, however, comes at a price -- exact methods for solvingthem are computationally very expensive and thus applicable in practice only tovery simple problems. We focus on efficient approximation (heuristic) methodsthat attempt to alleviate the computational problem and trade off accuracy forspeed. We have two objectives here. First, we survey various approximationmethods, analyze their properties and relations and provide some new insightsinto their differences. Second, we present a number of new approximationmethods and novel refinements of existing techniques. The theoretical resultsare supported by experiments on a problem from the agent navigation domain.

Value-Function Approximations for Partially Observable Markov Decision Processes