A Survey of solution techniques for the partially observed Markov decision process
Annals of Operations Research
Linear programming, the simplex algorithm and simple polytopes
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Region-based incremental pruning for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
An MDP-Based Recommender System
The Journal of Machine Learning Research
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
The permutable POMDP: fast solutions to POMDPs for preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Forward search value iteration for POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
icLQG: combining local and global optimization for control in information space
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in 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
Prioritizing Point-Based POMDP Solvers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We address the pruning or filtering problem, encountered in exact value iteration in POMDPs and elsewhere, in which a collection of linear functions is reduced to the minimal subset retaining the same maximal surface. We introduce the Skyline algorithm, which traces the graph corresponding to the maximal surface. The algorithm has both a complete and an iterative version, which we present, along with the classical Lark's algorithm, in terms of the basic dictionary-based simplex iteration from linear programming. We discuss computational complexity results, and present comparative experiments on both randomly-generated and well-known POMDP benchmarks.