Point-based value iteration for constrained POMDPs

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 1968-1974

  Kim, Dongho ^a   Lee, Jaesong ^a   Kim, Kee Eung ^a   Poupart, Pascal ^b  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE DYNAMIC PROGRAMMING; APPROXIMATE METHODS; MULTIPLE OBJECTIVES; OPTIMAL POLICIES; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; POINT-BASED METHODS; POINT-BASED VALUE ITERATIONS; RANDOMIZED POLICIES;

ARTIFICIAL INTELLIGENCE; OPTIMIZATION;

ITERATIVE METHODS;

EID: 84881060324     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-329     Document Type: Conference Paper

References (15)

1. E. Altman. Constrained Markov Decision Processes. Chapman & Hall/CRC, 1999.
2. A. Cassandra, M. Littman, and N. Zhang. Incremental pruning: A simple, fast, exact method for partially observable Markov decision processes. In Proceedings of UAI, 1997.
3. E. A. Feinberg and A. B. Piunovskiy. Nonatomic total rewards Markov decision processes with multiple criteria. Journal of Mathematical Analysis and Applications, 273(1):93-111, 2002.
4. M. Hauskrecht. Value-function approximations for partially observable Markov decision processes. Journal of Artificial Intelligence Research, 13:33-94, 2000. (Pubitemid 33682083)
5. J. D. Isom, S. P. Meyn, and R. D. Braatz. Piecewise linear dynamic programming for constrained POMDPs. In Proceedings of AAAI, 2008.
6. L. P. Kaelbling, M. L. Littman, and A. R. Cassandra. Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101:99-134, 1998.
7. N. Meuleau, E. Benazera, R. I. Brafman, E. A. Hansen, and Mausam. A heuristic search approach to planning with continuous resources in stochastic domains. Journal of Artificial Intelligence Research, 34:27-59, 2009.
8. G. E. Monahan. A survey of partially observable Markov decision-processes: Theory, models, and algorithms. Management Science, 28(1):1-16, 1982.
9. J. Pineau, G. Gordon, and S. Thrun. Anytime point-based approximations for large POMDPs. Journal of Artificial Intelligence Research, 27:335-380, 2006.
10. A. B. Piunovskiy and X. Mao. Constrained Markovian decision processes: the dynamic programming approach. Operations Research Letters, 27(3):119-126, 2000. (Pubitemid 32033826)
11. K. W. Ross and R. Varadarajan. Markov decision-processes with sample path constraints - the communicating case. Operations Research, 37(5):780-790, 1989. (Pubitemid 20599543)
12. K. W. Ross and R. Varadarajan. Multichain Markov decision-processes with a sample path constraint - a decomposition approach. Mathematics of Operations Research, 16(1):195-207, 1991.
13. J. D. Williams and S. Young. Partially observable Markov decision processes for spoken dialog systems. Computer Speech and Language, 21(2):393-422, 2007. (Pubitemid 44709839)
14. J.Williams, P. Poupart, and S. Young. Factored partially observable Markov decision processes for dialogue management. In Proceedings of IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems, 2005.
15. Q. Zhao, L. Tong, A. Swami, and Y. X. Chen. Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework. IEEE Journal on Selected Areas in Communications, 25(3):589-600, 2007.