Linear stochastic systems
Complexity Issues in Markov Decision Processes
COCO '98 Proceedings of the Thirteenth Annual IEEE Conference on Computational Complexity
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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This paper introduces a new filtering technique to speed up computation for finding exact policies for Partially Observable Markov Decision Problems (POMDP). We consider a new technique, called Scan Line Filter (SCF) for the Incremental Pruning (IP) POMDP exact solver to introduce an alternative method to Linear Programming (LP) filter. This technique takes its origin from the scan line method in computer graphics. By using a vertical scan line or plane, we show that a high-quality exact POMDP policy can be found easily and quickly. In this paper, we tested this new technique against the popular Incremental Pruning (IP) exact solution method in order to measure the relative speed and quality of our new method. We show that a high-quality POMDP policy can be found in lesser time in some cases. Furthermore, SCF has solutions for several POMDP problems that LP could not converge to in 12 hours.