Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Complexity Issues in Markov Decision Processes
COCO '98 Proceedings of the Thirteenth Annual IEEE Conference on Computational Complexity
The Witness Algorithm: Solving Partially Observable Markov Decision Processes
The Witness Algorithm: Solving Partially Observable Markov Decision Processes
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Tractable planning under uncertainty: exploiting structure
Tractable planning under uncertainty: exploiting structure
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
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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In recent years, there has been significant interest in developing techniques for finding policies for Partially Observable Markov Decision Problems (POMDPs). This paper introduces a new POMDP filtering technique that is based on Incremental Pruning [1], but relies on geometries of hyperplane arrangements to compute for optimal policy. This new approach applies notions of linear algebra to transform hyperplanes and treat their intersections as witness points [5]. The main idea behind this technique is that a vector that has the highest value at any of the intersection points must be part of the policy. IPBS is an alternative of using linear programming (LP), which requires powerful and expensive libraries, and which is subjected to numerical instability.