Solving transition independent decentralized Markov decision processes

Journal of Artificial Intelligence Research

Volumn 22, Issue , 2004, Pages 423-455

  Becker, Raphen ^a   Zilberstein, Shlomo ^a   Lesser, Victor ^a   Goldman, Claudia V ^a  

^a University of Massachusetts Amherst   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER SUPPORTED COOPERATIVE WORK; DECISION MAKING; INTELLIGENT AGENTS; MULTI AGENT SYSTEMS; PROBLEM SOLVING;

COLLABORATING AGENTS; COLLABORATIVE MULTI-AGENT SYSTEMS; MARKOV DECISION PROCESSES (MDPS); STRUCTURED GLOBAL REWARD FUNCTION;

MARKOV PROCESSES;

EID: 27344432831     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1497     Document Type: Article

References (31)

1. Becker, R., Zilberstein, S., & Lesser, V. (2004). Decentralized Markov decision processes with structured transitions. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, Vol. 1, pp. 302-309, New York City, New York. ACM Press.
2. Becker, R., Zilberstein, S., Lesser, V., & Goldman, C. V. (2003). Transition-independent decentralized Markov decision processes. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 41-48, Melbourne, Australia. ACM Press.
3. Bernstein, D. S., Givan, R., Immerman, N., & Zilberstein, S. (2002). The complexity of decentralized control of Markov decision processes. Mathematics of Operations Research, 27(4), 819-840.
4. Boutilier, C. (1999). Sequential optimality and coordination in multiagent systems. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 478-485.
5. Decker, K., & Lesser, V. (1993). Quantitative modeling of complex environments. International Journal of Intelligent Systems in Accounting, Finance and Management. Special Issue on Mathematical and Computational Models and Characteristics of Agent Behaviour, 2, 215-234.
6. Ghavamzadeh, M., & Mahadevan, S. (2002). A multiagent reinforcement learning algorithm by dynamically merging Markov decision processes. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi Agent Systems, Bologna, Italy. ACM Press.
7. Goldman, C. V., & Zilberstein, S. (2003). Optimizing information exchange in cooperative multi-agent systems. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 137-144, Melbourne, Australia. ACM Press.
8. Goldman, C. V., &: Zilberstein, S. (2004). Decentralized control of cooperative systems: Categorization and complexity analysis. To appear in Journal of Artificial Intelligence Research.
9. Guestrin, C., Venkataraman, S., &: Koller, D. (2002). Context specific multiagent coordination and planning with factored MDPs. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 253-259, Edmonton, Canada.
10. Hansen, E., Bernstein, D. S., &: Zilberstein, S. (2004). Dynamic programming for partially observable stochastic games. Proceedings of the Ninteenth National Conference on Artificial Intelligence, 709-715.
11. Hsu, K., & Marcus, S. I. (1982). Decentralized control of finite state Markov processes. IEEE Transactions on Automatic Control, 27(2), 426-431.
12. Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101, 99-134.
13. Liu, J., & Sycara, K. P. (1995). Exploiting problem structure for distributed constraint optimization. In Proceedings of the First International Conference on Multi-Agent Systems, pp. 246-253.
14. Mailler, R., & Lesser, V. (2004). Solving distributed constraint optimization problems using cooperative mediation. Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, 438-445.
15. Modi, P. J., Shen, W.-M., Tambe, M., & Yokoo, M. (2003). An asynchronous complete method for distributed constraint optimization. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 161-168.
16. Mundhenk, M., Goldsmith, J., Lusena, C., & Allender, E. (2000). Complexity of finitehorizon Markov decision process problems. Journal of the ACM, 47(4), 681-720.
17. Nair, R., Tambe, M., Yokoo, M., Pynadath, D., & Marsella, S. (2003). Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence.
18. Ooi, J. M., & Wornell, G. W. (1996). Decentralized control of a multiple access broadcast channel: Performance bounds. In Proceedings of the 35th Conference on Decision and Control, pp. 293-298.
19. Papadimitriou, C. H., & Tsitsiklis, J. (1982). On the complexity of designing distributed protocols. Information and Control, 53, 211-218.
20. Papadimitriou, C. H., & Tsitsiklis, J. (1986). Intractable problems in control theory. SIAM Journal on Control and Optimization, 24(4), 639-654.
21. Papadimitriou, C. H., & Tsitsiklis, J. (1987). The complexity of Markov decision processes. Mathematics of Operations Research, 12(3), 441-450.
22. Peshkin, L., Kim, K.-E., Meuleau, N., & Kaelbling, L. P. (2000). Learning to cooperate via policy search. In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 489-496, San Francisco, CA. Morgan Kaufmann.
23. Pynadath, D. V., & Tambe, M. (2002). The communicative multiagent team decision problem: Analyzing teamwork theories and models. Journal of Artificial Intelligence Research, 16, 389-423.
24. Shen, J., Lesser, V., &: Carver, N. (2003). Minimizing communication cost in a distributed Bayesian network using a decentralized MDP. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 678-685, Melbourne, Australia. ACM Press.
25. Tumer, K., Agogino, A. K., & Wolpert, D. H. (2002). Learning sequences of actions in collectives of autonomous agents. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi Agent Systems.
26. Washington, R., Golden, K., Bresina, J., Smith, D., Anderson, C., & Smith, T. (1999). Autonomous rovers for Mars exploration. In Proceedings of the IEEE Aerospace Conference.
27. Wolpert, D., Wheeler, K., & Tumer, K. (1999). General principles of learning-based multiagent systems. In Proceedings of the Third International Conference on Autonomous Agents, pp. 77-83, Seattle, WA.
28. Xuan, P., & Lesser, V. (2002). Multi-agent policies: From centralized ones to decentralized ones. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 1098-1105. ACM Press.
29. Xuan, P., Lesser, V., & Zilberstein, S. (2001). Communication decisions in multi-agent cooperation: Model and experiments. In Proceedings of the Fifth International Conference on Autonomous Agents, pp. 616-623, Montreal, Canada. ACM Press.
30. Yokoo, M., & Durfee, E. H. (1991). Distributed constraint optimization as a formal model of partially adversarial cooperation. Tech. rep. CSE-TR-101-91, The University of Michigan, Ann Arbor, Michigan.
31. Zilberstein, S., Washington, R., Bernstein, D. S., & Mouaddib, A. I. (2002). Decisiontheoretic control of planetary rovers. In Beetz, M., et al. (Eds.), Plan-Based Control of Robotic Agents, No. 2466, in Lecture Notes in Artificial Intelligence, pp. 270-289. Springer.