Planning in POMDPs using multiplicity automata

Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005

Volumn , Issue , 2005, Pages 185-192

  Even Dar, Eyal ^a   Kakade, Sham M ^b   Mansour, Yishay ^a  

^a TEL AVIV UNIVERSITY   (Israel)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATA THEORY;

NUMBER OF STATE; OPERATION RESEARCH; PLANNING ALGORITHMS; PREDICTIVE STATE REPRESENTATION;

ROBOTS;

EID: 33750367918     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Baruch Awerbuch and Robert D. Kleinberg. Adaptive routing with end-to-end feedback: distributed learning and geometric approaches. In STOC, pages 45-53. ACM Press, 2004.
2. Amos Beimel, Francesco Bergadano, Nader H. Bshouty, Eyal Kushilevitz, and Stefano Varricchio. Learning functions represented as multiplicity automata. J. ACM, 47 (3):506-530, 2000.
3. Dimitri P. Bertsekas and John N. Tsitsiklis. Neuro-Dynamic Programming. Athena Scientific, Belmont, MA, 1996.
4. B. Bonet. An ε-optimal grid-based algorithm for Partially Observable Markov Decision Processes. In Proc. 19th International Conf. on Machine Learning, pages 51-58. Morgan Kaufmann, 2002.
5. R. Brafman. A heuristic variable grid solution method for pomdps. In AAAI-97, pages 727-733, 1997.
6. J.W Carlyle and A. Paz. Realization by stochastic finite automaton. J. Comput. Syst. Sci., 5:26-40, 1971.
7. M. Fliess. Matrices dehankel. J. Math. Pures Appl., 53: 197-222, 1974.
8. M. Hauskrecht. A heuristic variable-grid solution method for pomdps. In AAAI-97, pages 734-739, 1997.
9. Michael Littman, Richard Sutton, and Satinder Singh. Predictive representations of state. In Advances in Neural Information Processing Systems 14 (NIPS), pages 1555-1561, 2001.
10. W. S. Lovejoy. Computationally feasible bounds for partially observed markov decision processes. Operations Research, 39(1):162-175, 1991.
11. C. Lusena, J. Goldsmith, and M. Mundhenk. Nonapproximability results for partially observable markov decision processes. Journal of Artificial Intelligence Research, 14:83-103, 2001. (Pubitemid 33738060)
12. M.P Shlitzenberger. On the definition of a family of automata. Inf. Control, 4:245-270, 1961.
13. Satinder Singh and David Cohn. How to dynamically merge markov decision processes. In Advances in Neural Information Processing Systems 10 (NIPS), pages 1057-1063, 1998.
14. Satinder Singh, Michael R. James, and Matthew R. Rudary. Predictive state representations: A new theory for modeling dynamical systems. In Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (UAI), pages 512-519, 2004.
15. Satinder Singh, Michael Littman, Nicholas Jong, David Pardoe, and Peter Stone. Learning predictive state representations. In Proceedings of the Twentieth International Conference on Machine Learning (ICML), pages 712-719, 2003.
16. Satinder P. Singh and Richard C. Yee. An upper bound on the loss from approximate optimal-value functions. Machine Learning, 16(3):227-233, 1994.
17. R. Sutton and A. Barto. Reinforcement Learning. MIT Press, Cambridge, MA, 1998.