Preprocessing for Point-Based Algorithms of POMDPs

Point-based algorithms are a class of approximate methods for Partially Observable Markov Decision Processes (POMDPs). They do backup operators on a belief set only. This paper will propose a preprocessing method for point-based algorithms (PPBA). This method preprocesses each sampled belief point, and before generating alpha-vectors it estimates which action and alpha-vectors to be selected first, in so doing repeated computing is eliminated. Base-alpha is also defined in this paper, which cancels meaningless computing with sparseness of problem.