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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs |
| Organization Unit |
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| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI) |
| Event Type | conference |
| Event Location | Vancouver, Canada |
| Event Start Date | July 19 - 2007 |
| Event End Date | July 22 - 2007 |
| Place of Publication | Vancouver, Canada |
| Abstract Text | Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in solving decentralized POMDPs with large horizons. We generalize the algorithm and improve its scalability by reducing the complexity with respect to the number of observations from exponential to polynomial. We derive error bounds on solution quality with respect to this new approximation and analyze the convergence behavior. To evaluate the effectiveness of the improvements, we introduce a new, larger benchmark problem. Experimental results show that despite the high complexity of decentralized POMDPs, scalable solution techniques such as MBDP perform surprisingly well. |
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