Solving planning under uncertainty: Quantitative and qualitative approach

Advances in Soft Computing

Volumn 42, Issue , 2007, Pages 612-620

  Yin, Minghao ^a   Wang, Jianan ^a   Gu, Wenxiang ^a  

^a NORTHEAST NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 58149268855     PISSN: 16153871     EISSN: 18600794     Source Type: Book Series    
DOI: 10.1007/978-3-540-72434-6_62     Document Type: Article

References (22)

1. Barto, A. G. et al (1995) Learning to act using real-time dynamic programming. Artificial Intelligence 72:81-138.
2. Bagnell, J. A. et al (2001) Solving uncertainty Markov decision problems. Technical Report CMU-RI-TR-01-25, Robotics Institute, Carnegie Mellon University.
3. Bertesekas, D.P. and Tsitsiklis (1996), J.N. Neurodynamic Programming, Athena Scientific.
4. Blai Bonet and Hector Geffner (2003) Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming. In Proc. of ICAPS-03. Trento, Italy, AAAI Press, pp 12-21.
5. Buffet, O. (2005), Planning with robust (L)RTDP. In: Proc. of IJCAI-05.
6. C. da Costa Pereira, F. Garcia, J. Lang and R. Martin-Clouaire (1997) Planning with graded nondeterministic actions: A possibilistic approach, International Journal of Intelligent Systems, 12 (11/12), 935-962.
7. C. da Costa Pereira, F. Garcia, J. Lang and R. Martin-Clouaire (1999) Possibilistic planning: Representation and complexity. Recent Advances in Planning (Proceedings of ECP'99), Lectures Notes in Artificial Intelligence, Springer Verlag, 1997, 143-155.
8. Dubois, D. and Prade H. (1995) Possibility theory as a basis of qualitative decision theory. In: Proc. of the IJCAI-95
9. Dubois, D. etc (1998) Logical representation and computation of optimal decisions in a qualitative setting. In: Proc. of AAAI-98, California, AAAI press.
10. Dubois, D. etc. (2003) Qualitative decision theory with preference relations and comparative uncertainty: An axiomatic approach, Artificial Intelligence 148: 219-260.
11. Fargier, H. etc. (1998) Towards qualitative approaches to multi-state decision making. International Journal of Approximate Reasoning 19, 441-471.
12. Fargier, H. etc.(2005) Qualitative decision under uncertainty: Back to expected utility, Artificial Intelligence 164:245-280.
13. Feng zhengzhu, Hansen, Eric A.(2002) Symbolic Heuristic Search for factored Markov decision processes, In: Proceedings of AAAI 2002.
14. Givan, R., etc. (2000) Bounded parameter markov decision processes. Artificial Intelligence, 122(1-2): 71-109.
15. Hansen, Eric A., Zilberstein, S.(2001) LAO*: A heuristic search algorithm that finds solutions with loops, Artificial Intelligence 129: 35-62.
16. Hosaka, M. (2001) etc.: Controlled markov set-chains under average criteria. Applied Mathematics and Computation, 120(1-3):195-209
17. Munos, R.(2001) Efficient resources allocation for markov decision processes. In: Proc. of NIPS 2001.
18. Nilim, A. and Ghaoui, L. El. (2004) Robustness in markov decision problems with uncertainty transition matrices. In: Proc. of NIPS 2004.
19. Sabbadin. R. (2001) Possibilistic markov decision processes. Engineering of Artificial Intelligence 14:287-300.
20. Tan, S. W. etc. (1994) Qualitative decision theory. In: Proc of AAAI-94, California, AAAI press.
21. Russell S. and Norvig P (2003) Artificial Intelligence: A modern approach 2nd Edition, Prentice Hall.
22. Blai Bonet and Hector Geffner (2005) An Algorithm Better than AO*? In Proc. of AAAI-05 Pittsburgh, Pennsylvania. AAAI Press. Pages 1343-1348.