Incremental policy generation for finite-horizon DEC-POMDPs

ICAPS 2009 - Proceedings of the 19th International Conference on Automated Planning and Scheduling

Volumn , Issue , 2009, Pages 2-9

  Amato, Chistopher ^a   Dibangoye, Jilles Steeve ^b   Zilberstein, Shlomo ^a  

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

^b UNIVERSITÉ LAVAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE DYNAMIC PROGRAMMING; DYNAMIC PROGRAMMING ALGORITHM; INITIAL STATE; MULTI-AGENT PLANNING; OPTIMAL DYNAMICS; OPTIMAL SOLUTIONS; PERFORMANCE GAIN; PLANNING HORIZONS; POLICY GENERATION; REACHABILITY ANALYSIS; RESOURCE CONSUMPTION; RESOURCE USAGE; STATE SPACE;

ALGORITHMS; OPTIMIZATION; SCALABILITY;

DYNAMIC PROGRAMMING;

EID: 77958561050     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Amato, C., and Zilberstein, S. 2009. Achieving goals in decentralized POMDPs. In Proc. of the Eighth Int. Joint Conf. on Autonomous Agents and Multiagent Systems.
2. Bernstein, D. S.; Amato, C.; Hansen, E. A.; and Zilberstein, S. 2009. Policy iteration for decentralized control of Markov decision processes. Journal of AI Research 34.
3. Bernstein, D. S.; Hansen, E.; and Zilberstein, S. 2005. Bounded policy iteration for decentralized POMDPs. In Proc. of the Nineteenth Int. Joint Conf. on Artificial Intelligence.
4. Boularias, A., and Chaib-draa, B. 2008. Exact dynamic programming for decentralized POMDPs with lossless policy compression. In Proc. of the Eighteenth Int. Conf. on Automated Planning and Scheduling.
5. Carlin, A., and Zilberstein, S. 2008. Value-based observation compression for DEC-POMDPs. In Proc. of the Seventh Int. Joint Conf. on Autonomous Agents and Multiagent Systems.
6. Cassandra, A.; Littman, M. L.; and Zhang, N. L. 1997. Incremental pruning: A simple, fast, exact method for partially observable Markov decision processes. In Proc. of the Thirteenth Conf. on Uncertainty in Artificial Intelligence.
7. Dibangoye, J. S.; Mouaddib, A.; and Chaib-draa, B. 2009. Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs. In Proc. of the Eighth Int. Joint Conf. on Autonomous Agents and Multiagent Systems.
8. Hansen, E. A.; Bernstein, D. S.; and Zilberstein, S. 2004. Dynamic programming for partially observable stochastic games. In Proc. of the Nineteenth National Conf. on Artificial Intelligence.
9. Nair, R.; Pynadath, D.; Yokoo, M.; Tambe, M.; and Marsella, S. 2003. Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings. In Proc. of the Nineteenth Int. Joint Conf. on Artificial Intelligence.
10. Oliehoek, F. A.; Spaan, M. T. J.; and Vlassis, N. 2008. Optimal and approximate Q-value functions for decentralized POMDPs. Journal of AI Research 32.
11. Oliehoek, F. A.; Whiteson, S.; and Spaan, M. T. J. 2009. Lossless clustering of histories in decentralized POMDPs. In Proc. of the Eighth Int. Joint Conf. on Autonomous Agents and Multiagent Systems.
12. Seuken, S., and Zilberstein, S. 2007a. Improved memory-bounded dynamic programming for decentralized POMDPs. In Proc. of the Twenty-Third Conf. on Uncertainty in Artificial Intelligence.
13. Seuken, S., and Zilberstein, S. 2007b. Memory-bounded dynamic programming for DEC-POMDPs. In Proc. of the Twentieth Int. Joint Conf. on Artificial Intelligence.
14. Seuken, S., and Zilberstein, S. 2008. Formal models and algorithms for decentralized control of multiple agents. Journal of Autonomous Agents and Multi-Agent Systems 17(2).
15. Szer, D., and Charpillet, F. 2006. Point-based dynamic programming for DEC-POMDPs. In Proc. of the Twenty-First National Conf. on Artificial Intelligence. (Pubitemid 44705443)
16. Szer, D.; Charpillet, F.; and Zilberstein, S. 2005. MAA*: A heuristic search algorithm for solving decentralized POMDPs. In Proc. of the Twenty-First Conf. on Uncertainty in Artificial Intelligence.