The domestic robot—a friendly cognitive system takes care of your home
Ambient intelligence
A Navigation System for Assistant Robots Using Visually Augmented POMDPs
Autonomous Robots
A model approximation scheme for planning in partially observable stochastic domains
Journal of Artificial Intelligence Research
Distributed spectrum sensing and access in cognitive radio networks with energy constraint
IEEE Transactions on Signal Processing
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Incremental methods for computing bounds in partially observable Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Decentralized MDPs with sparse interactions
Artificial Intelligence
Planning with partially observable Markov decision processes: advances in exact solution method
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Quantitative access control with partially-observable Markov decision processes
Proceedings of the second ACM conference on Data and Application Security and Privacy
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The main objective of this report is to provide implementation details for the more popular exact algorithms for solving finite horizon partially observable Markov decision processes (POMDPs). Along with the existing algorithms, a new algorithm, Witness, is proposed that has empirically been faster than the existing exact techniques. In addition to algorithmic details, the basic formulas and concepts of POMDPs are presented, as well as explanations and discussion about the basic form of POMDP solutions. This document is aimed at those who do not have a lot of experience with the techniques or concepts of POMDPs.