Anytime planning for decentralized POMDPs using Expectation Maximization

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Volumn , Issue , 2010, Pages 294-301

  Kumar, Akshat ^a   Zilberstein, Shlomo ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; INFERENCE ENGINES; MULTI AGENT SYSTEMS; STOCHASTIC SYSTEMS;

BENCHMARK DOMAINS; EXPECTATION - MAXIMIZATIONS; EXPECTATION-MAXIMIZATION ALGORITHMS; INFERENCE TECHNIQUES; INHERENT COMPLEXITY; OPTIMIZATION PROBLEMS; SEQUENTIAL DECISION MAKING; STATE OF THE ART;

MAXIMUM PRINCIPLE;

EID: 80053161304     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. C. Amato, D. S. Bernstein, and S. Zilberstein. Optimizing -xed-size stochastic controllers for POMDPs and decentralized POMDPs. JAAMAS, 2009.
2. H. Attias. Planning by probabilistic inference. In Workshop on AISTATS, 2003.
3. R. Becker, S. Zilberstein, V. Lesser, and C. V. Goldman. Solving transition independent decentralized markov decision processes. JAIR, 22:423-455, 2004. (Pubitemid 41525892)
4. D. S. Bernstein, C. Amato, E. A. Hansen, and S. Zilberstein. Policy iteration for decentralized control of Markov decision processes. JAIR, 34:89-132, 2009.
5. D. S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein. The complexity of decentralized control of Markov decision processes. J. MOR, 27:819-840, 2002.
6. J. Dean and S. Ghemawat. MapReduce: a exible data processing tool. CACM, 53(1):72-77, 2010.
7. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical society, Series B, 39(1):1-38, 1977.
8. J. S. Dibangoye, A.-I. Mouaddib, and B. Chaib-draa. Point-based incremental pruning heuristic for solving -nite-horizon DEC-POMDPs. In AAMAS, pages 569-576, 2009.
9. P. E. Gill, W. Murray, and M. A. Saunders. SNOPT: An SQP algorithm for large-scale constrained optimization. SIOPT, 12(4):979-1006, 2002.
10. M. Hoffman, H. Kueck, N. de Freitas, and A. Doucet. New inference strategies for solving Markov decision processes using reversible jump MCMC. In UAI, 2009.
11. A. Kumar and S. Zilberstein. Point based backup for decentralized POMDPs: Complexity and new algorithms. In AAMAS, pages 1315-1322, 2010.
12. M. Mundhenk, J. Goldsmith, C. Lusena, and E. Allender. Complexity of -nite-horizon Markov decision process problems. J. ACM, 47(4):681-720, 2000.
13. R. Nair, P. Varakantham, M. Tambe, and M. Yokoo. Networked distributed POMDPs: A synthesis of distributed constraint optimization and POMDPs. In AAAI, pages 133-139, 2005. (Pubitemid 43006767)
14. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate Q-value functions for decentralized POMDPs. JAIR, 32:289-353, 2008.
15. J. Pineau, G. Gordon, and S. Thrun. Anytime point- based approximations for large POMDPs. JAIR, 27:335-380, 2006.
16. S. Seuken and S. Zilberstein. Memory-bounded dynamic programming for DEC-POMDPs. In IJCAI, pages 2009-2015, 2007.
17. T. Smith and R. Simmons. Heuristic search value iteration for POMDPs. In UAI, pages 520-527, 2004.
18. M. Toussaint, L. Charlin, and P. Poupart. Hierarchical POMDP controller optimization by likelihood maximization. In UAI, pages 562-570, 2008.
19. M. Toussaint, S. Harmeling, and A. Storkey. Probabilistic inference for solving (PO)MDPs. Technical Report EDIINF-RR-0934, University of Edinburgh, School of Informatics, 2006.
20. M. Toussaint and A. J. Storkey. Probabilistic inference for solving discrete and continuous state markov decision processes. In ICML, pages 945-952, 2006.