Planning under risk and knightian uncertainty

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 2023-2028

  Trevizan, Felipe W ^a   De Barros, Leliane N ^a   Cozman, Fábio G ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

BELLMAN'S PRINCIPLE; KNIGHTIAN UNCERTAINTY; NONDETERMINISTIC PLANNING; PROBABILISTIC PLANNING;

ARTIFICIAL INTELLIGENCE; MODEL CHECKING;

UNCERTAINTY ANALYSIS;

EID: 84880854741     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. B. Anrig, R. Bissig, R. Haenni, J. Kohlas, and N. Lehmann. Probabilistic argumentation systems: Introduction to assumption-based modeling with ABEL. Technical Report 99-1, Institute of Informatics, University of Fribourg, 1999.
2. R. E. Bellman. Dynamic Programming. Princeton University Press, Princeton, New Jersey, 1957.
3. J.O. Berger. Statistical Decision Theory and Bayesian Analysis. Springer-Verlag, 1985.
4. D.P. Bertsekas. Dynamic programming and optimal control. Athena Scientific Belmont, Mass, 1995.
5. B. Bonet and H. Geffner. Labeled RTDP: Improving the convergence of real-time dynamic programming. In Proc. of the 13th ICAPS, pages 12-21, Trento, Italy, 2003. AAAI Press.
6. B. Bonet and H. Geffner. Learning Depth-First Search: A unified approach to heuristic search in deterministic and non-deterministic settings, and its application to MDPs. In Proc. of the 16th ICAPS, 2006.
7. O. Buffet and D. Aberdeen. Robust planning with (L)RTDP. In Proc. of the 19th IJCAI, pages 1214-1219, 2005.
8. F.G. Cozman. Graphical models for imprecise probabilities. International Journal of Approximate Reasoning, 39(2-3):167-184, 2005. (Pubitemid 40467339)
9. T. Eiter and T. Lukasiewicz. Probabilistic reasoning about actions in nonmonotonic causal theories. In Proc. of the 19th UAI, pages 192-199, 2003.
10. E. Fagiuoli and M. Zaffalon. 2U: An exact interval propagation algorithm for polytrees with binary variables. Artificial Intelligence, 106(1):77-107, 1998. (Pubitemid 128402351)
11. R. Givan, S. M. Leach, and T. Dean. Bounded-parameter Markov decision processes. Artificial Intelligence, 122(1-2):71-109, 2000.
12. D. Harmanec. A generalization of the concept of Markov decision process to imprecise probabilities. In ISIPTA, pages 175-182, 1999.
13. J.B. Kadane, M.J. Schervish, and T. Seidenfeld. Rethinking the Foundations of Statistics. Cambridge University Press, 1999.
14. N. Karmarkar. A new polynomial-time algorithm for linear programming. In Procs of the 16th annual ACM symposium on Theory of computing, pages 302-311. ACM Press New York, NY, USA, 1984.
15. F.H. Knight. Risk, Uncertainty, and Profit. Hart, Schaffner & Marx; Houghton Mifflin Company, Boston, 1921.
16. M. Kojima, S. Mizuno, and A. Yoshise. A primal-dual interior point algorithm for linear programming. In Progress in Mathematical Programming: Interior-point and related methods, pages 29-47. Springer-Verlag, 1988.
17. I. Levi. The Enterprise of Knowledge. MIT Press, 1980.
18. D. Luce and H. Raiffa. Games and Decisions. Dover edition, Mineola, 1957.
19. N.J. Nilsson. Probabilistic logic. Artificial Intelligence, 28:71-87, 1986.
20. Christos H. Papadimitriou. Computational Complexity. Addison-Wesley, 1994.
21. P. Perny, O. Spanjaard, and P.Weng. Algebraic Markov decision processes. In Proc. of the 19th IJCAI, pages 1372-1377, 2005.
22. J. K. Satia and R. E. Lave Jr. Markovian decision processes with uncertain transition probabilities. Operations Research, 21(3):728-740, 1973.
23. G. Shafer. A Mathematical Theory of Evidence. Princeton University Press, 1976.
24. F. W. Trevizan, F. G. Cozman, and L. N. de Barros. Unifying nondeterministic and probabilistic planning through imprecise markov decision processes. In Proc. of the 10th IBERAMIA/18th SBIA, pages 502-511, 2006.
25. M. Troffaes. Decisions making with imprecise probabilities: a short review. The SIPTA Newsletter, 2(1):4-7, 2004.
26. P. Walley. Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London, 1991.
27. C. C. White III and H. K. Eldeib. Markov decision processes with imprecise transition probabilities. Operations Research, 42(4):739-749, 1994.