Modeling Probabilistic Actions for Practical Decision-Theoretic Planning

Proceedings of the 3rd Artificial Intelligence Planning Systems Conference, AIPS 1996

Volumn , Issue , 1996, Pages 62-69

  Doan, AnHai ^a  

^a University of Wisconsin Milwaukee   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; SEMANTICS;

ACTION MODELING; DECISION-THEORETIC; DECISION-THEORETIC PLANNING; EXPECTED UTILITY; MASS ASSIGNMENTS; PLANNING PROBLEM; PROBABILISTICS; PROBABILITY INTERVALS; PROBABILITY: DISTRIBUTIONS; UNCERTAINTY;

PROBABILITY DISTRIBUTIONS;

EID: 85015426015     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Breese, J., and Fertig, K. 1991. Decision making with interval influence diagreun. Uncertainty in Atrificial Intelligence 6. P.P Bonissone, M. Henrion, L. N. Kanal and J. F. Lemmer (Editors).
2. Chrisman, L. Personal communications.
3. Chrisman, L. 1992. Abstract probabilistic modeling of action. In Proceedings of the First International Conference on Artificial Intelligence Planning Systems, 28-36.
4. Gormen, T.; Leiserson, C. E.; and Rivest, R. L. 1990. Introduction to Algorithms. Cambridge, MA: The M I T Press.
5. Dean, T.; Kaelbling, L. P.; Kirman, J.; and Nicholson, A. 1993. Planning with deadlines in stochastic domains. In Proceedings of the Eleventh National Conference on Artificial Intelligence, 574-579.
6. DeRobertis, L., and Hartigan, J. 1981. Bayesian inference using intervals of measures. The Annals of Statistics 9(2):235-244.
7. Doan, A. 1996. An abstraction-based approach to decision-theoretic planning for partially observable metric domsdns. Technical report, Dept. of Elect. Eng. & Computer Science, University of Wisconsin-Milwaukee. Masters Thesis.
8. Frisch, A., and Haddawy, P. 1994. Anytime deduction for probabilistic logic. Artificial Intelligence 69(1-2):93-122.
9. Goldman, R., and Boddy, M. 1994. Epsilon-safe planning. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 253-261.
10. Haddawy, P., and Doan, A. 1994. Abstracting probabilistic actions. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 270¬ 277.
11. Haddawy, P.; Doan, A.; and Goodwin, R. 1995. Efficient decision-theoretic planning: Techniques and empirical analysis. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 229¬ 236.
12. Kushmerick, N.; Hanks, S.; and Weld, D. 1993. An algorithm for probabilistic planning. Technical Report 93-06-03, Dept. of Computer Science and E n - gineering, University of Washington. (To appear in Artificial Intelligence).
13. Nilsson, N. 1986. Probabilistic logic. Artificial Intelligence 28:71-87.
14. Ramoni, M. 1995. Ignorant influence diagrams. In Proceedings of the International Joint Conference on Artificial Intelligence, 1869-1875.
15. Shafer, G. 1981. Constructive probability. Synthese 48:1-60.
16. Snow, P. 1986. Bayesian inference without point estimates. In National Conference on Artificial Intelligence, 233-237.
17. Strat, T. M. 1990. Decision analysis using belief functions. International Journal of Approximate Reasoning 4:319-417.