Trial-Based Dynamic Programming for Multi-Agent Planning

Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Volumn , Issue , 2010, Pages 908-914

  Wu, Feng ^a   Zilberstein, Shlomo ^b   Chen, Xiaoping ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b University of Massachusetts Amherst   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR PROGRAMMING; MULTI AGENT SYSTEMS;

COMPUTATIONAL SAVINGS; DYNAMIC PROGRAMMING ALGORITHM; LINEAR-PROGRAMMING; MULTI-AGENT PLANNING; MULTI-AGENT SETTING; PLANNING ALGORITHMS; POLICY EVALUATION; REUSE; SINGLE-AGENT; VALUE IMPROVEMENTS;

DYNAMIC PROGRAMMING;

EID: 84899439466     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Amato, C.; Dibangoye, J. S.; and Zilberstein, S. 2009. Incremental policy generation for finite-horizon DEC-POMDPs. In Proc. of the 19th Int'l Conf. on Automated Planning and Scheduling, 2-9.
2. Barto, A. G.; Bradtke, S. J.; and Singh, S. P. 1995. Learning to act using real-time dynamic programming. Artificial Intelligence 72(1-2):81-138.
3. Bernstein, D. S.; Zilberstein, S.; and Immerman, N. 2000. The complexity of decentralized control of Markov decision processes. In Proc. of the 16th Conf. on Uncertainty in Artificial Intelligence, 32-37.
4. Bernstein, D. S.; Hansen, E. A.; and Zilberstein, S. 2005. Bounded policy iteration for decentralized POMDPs. In Proc. of the 19th Int'l Joint Conf. on Artificial Intelligence, 1287-1292.
5. Bonet, B., and Geffner, H. 2009. Solving POMDPs: RTDP-Bel vs. point-based algorithms. In Proc. of the 21st Int'l Joint Conf. on Artificial Intelligence, 1641-1646.
6. Boutilier, C. 1996. Planning, learning and coordination in multiagent decision processes. In Proc. of the 6th Conf. on Theoretical Aspects of Rationality and Knowledge, 195-210.
7. Carlin, A., and Zilberstein, S. 2008. Value-based observation compression for DEC-POMDPs. In Proc. of the 7th Int'l Joint Conf. on Autonomous Agents and Multi-Agent Systems, 501-508.
8. 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 8th Int'l Joint Conf. on Autonomous Agents and Multi-Agent Systems, 569-576.
9. Geffner, H., and Bonet, B. 1998. Solving large POMDPs using real time dynamic programming. In AAAI Fall Symposium on POMDPs.
10. Hansen, E. A.; Bernstein, D. S.; and Zilberstein, S. 2004. Dynamic programming for partially observable stochastic games. In Proc. of the 19th National Conf. on Artificial Intelligence, 709-715.
11. Kaelbling, L. P.; Cassandra, A.; and Kurien, J. 1996. Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. In Proc. of IEEE/RSJ Int'l Conf. on Intelligent Robots and Systems, 963-972.
12. McMahan, H. B.; Likhachev, M.; and Gordon, G. J. 2005. Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees. In Proc. of the 23rd Int'l Conf. on Machine Learning, 569-576.
13. Nair, R.; Tambe, M.; Yokoo, M.; Pynadath, D. V.; and Marsella, S. 2003. Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings. In Proc. of the 18th Int'l Joint Conf. on Artificial Intelligence, 705-711.
14. Peshkin, L.; Kim, K.-E.; Meuleau, N.; and Kaelbling, L. P. 2000. Learning to cooperate via policy search. In Proc. of the 16th Conf. on Uncertainty in Artificial Intelligence, 489-496.
15. Pynadath, D. V., and Tambe, M. 2002. The communicative multiagent team decision problem: Analyzing teamwork theories and models. Journal of Artificial Intelligence Research 16:389-423.
16. Sanner, S.; Goetschalckx, R.; Driessens, K.; and Shani, G. 2009. Bayesian real-time dynamic programming. In Proc. of the 21st Int'l Joint Conf. on Artificial Intelligence, 1784-1789.
17. Seuken, S., and Zilberstein, S. 2007a. Improved memory-bounded dynamic programming for decentralized POMDPs. In Proc. of the 23rd Conf. on Uncertainty in Artificial Intelligence, 344-351.
18. Seuken, S., and Zilberstein, S. 2007b. Memory-bounded dynamic programming for DEC-POMDPs. In Proc. of the 20th Int'l Joint Conf. on Artificial Intelligence, 2009-2015.
19. Smith, T., and Simmons, R. G. 2006. Focused real-time dynamic programming for mdps: Squeezing more out of a heuristic. In Proc. of the 21st National Conf. on Artificial Intelligence, 1227-1232.
20. Szer, D., and Charpillet, F. 2006. Point-based dynamic programming for DEC-POMDPs. In Proc. of the 21st National Conf. on Artificial Intelligence, volume 2, 1233-1238.