This paper is about planning in stochastic domains by means of partially observable Markov decision processes (POMDPs). POMDPs are difficult to solve. This paper considers problems where one, although does not know the true state of the world, has a pretty good idea about it and uses such problem characteristics to transform POMDPs into approximately equivalent ones that are much easier to solve. We also propose a new algorithm for dynamic-programming updates (Littman et al 1995), the core problem in solving POMDPs and a new way to approximate the t-step optimal value function of a POMDP. Keywords: planning under uncertainty, partially observable Markov decision processes, problem characteristics, policy trees, parsimonious representation 1 Introduction To plan is to find a policy that will lead an agent to achieve a goal in the fewest number of steps possible. When the environment of the agent, sometimes referred to as the world, is completely observable and the effects of actions ..
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