Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Symbolic heuristic search value iteration for factored POMDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Forward search value iteration for POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Solving POMDPs: RTDP-bel vs. point-based algorithms
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Distributed planning in hierarchical factored MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale due to not exploiting the inherent structure of the organizations. We propose a new model called the \text it{OrgPOMDP} (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: (a) Introduce the OrgPOMDP model, (b) Present an algorithm to solve OrgPOMDP problems efficiently, and (c) Apply OrgPOMDPs to scenarios in an existing large organization, the Air and Space Operation Center (AOC). We conduct experiments and show that our OrgPOMDP approach results in greater scalability and greatly reduced runtime. In fact, as the size of the problem increases, we soon reach a point at which the OrgPOMDP approach continues to provide solutions while traditional POMDP methods cannot. We also provide an empirical evaluation to highlight the benefits of an organization implementing an OrgPOMDP policy.