Anytime coordination using separable bilinear programs

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2007, Pages 750-755

  Petrik, Marek ^a   Zilberstein, Shlomo ^a  

^a University of Massachusetts Amherst   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COVERAGE SET ALGORITHM (CSA); DECENTRALIZED DECISION PROBLEMS; OFFLINE ERROR BOUNDS; STRUCTURED INTERACTION;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS;

MULTI AGENT SYSTEMS;

EID: 36348940413     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Becker, R.; Zilberstein, S.; Lesser, V.; and Goldman, C. V. 2004. Solving transition independent decentralized Markov decision processes. Journal of Artificial Intelligence Research 22:423-455.
2. Bennett, K. P., and Mangasarian, O. L. 1992. Bilinear separation of two sets in n-space. Technical report, Computer Science Department, University of Wisconsin.
3. Emery-Montemerlo, R.; Gordon, G.; Schneider, J.; and Thrun, S. 2004. Approximate solutions for partially observable stochastic games with common payoffs. In Autonomous Agents and Multiagent Systems.
4. Horst, R., and Tuy, H. 1996. Global optimization: Deterministic approaches. Springer.
5. Kim, Y.; Nair, R.; Varakantham, P.; Tambe, M.; and Yokoo, M. 2006. Exploiting locality of interaction in networked distributed POMDPs. In AAAI Spring Symposium on Distributed Planning and Scheduling.
6. Lovejoy, W. S. 1991. Computationally feasible bounds for partially observed Markov decision processes. Operations Research 39:162-175.
7. Mangasarian, O. L. 1995. The linear complementarity problem as a separable bilinear program. Journal of Global Optimization 12:1-7.
8. Murty, K. G. 1988. Linear complementarity, linear and nonlinear programming. Helderman-Verlag.
9. Pang, J.-S.; Trinkle, J. G.; and Lo, G. 1996. A complementarity approach to a quasistatic rigid body motion problem. Journal of Computational Optimization and Applications 5(2): 139-154.
10. Petrik, M., and Zilberstein, S. 2007. Average reward decentralized Markov decision processes. In International Joint Conference on Artificial Intelligence. Pineau, J.;
11. Gordon, G.; and Thrun, S. 2006. Point-based approximations for fast POMDP solving. Jornal of Artificial Intelligence Research 27(SOCS-TR-2005.4): 335-380.
12. Puterman, M. L. 2005. Markov decision processes: Discrete stochastic dynamic programming. John Wiley & Sons, Inc.
13. Rosen, J. B. 1986. Solution of general LCP by 0-1 mixed integer programming. Technical Report Computer Science Tech. Report 8623, University of Minnesota, Minneapolis.
14. Seuken, S., and Zilberstein, S. 2005. Formal models and algorithms for decentralized control of multiple agents. Technical Report 2005-068, Computer Science Department, University of Massachusetts.
15. Seuken, S., and Zilberstein, S. 2007. Memory bounded dynamic programming for DEC-POMDPs. In International Joint Conference on Artificial Intelligence.
16. Vanderbei, R. J. 2001. Linear Programming: Foundations and Extensions. Second edition. Springer.