First-order Bayesian reasoning
This paper briefly discusses problems with traditional Bayesian networks, and previous attempts at overcoming those problems, as a motivation for formulating a first-order knowledge based approach to Bayesian inference. The proposed first-order knowledge based approach endeavours to address each of the traditional Bayesian network problems.
KeywordsBayesian Inference First-Order Logic Logic Probability Uncertainty
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- 1.F. Bacchus. Using first-order probability logic for the construction of Bayesian networks. Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, 1993.Google Scholar
- 2.J. S. Breese, R. P. Goldman, and M. P. Wellman. Introduction to the special section on knowledge-based construction of probabilistic and decision models. IEEE Transactions on Systems, Man, and Cybernetics, 24 (1), Nov. 1994.Google Scholar
- 3.I. G. Fabian and D. A. Lambert. First-order Bayesian reasoning for situation assessment. Technical report, DSTO report in progress, 1998.Google Scholar
- 4.S. Glesner and D. Koller. Constructing flexible dynamic belief networks form firstorder probabilistic knowledge bases. Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 1995.Google Scholar
- 5.P. Haddawy. Generating Bayesian networks from probability logic bases. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 262–269, 1994.Google Scholar
- 6.D. A. Lambert. Assets with Attitude. Technical report, DSTO report in progress, 1998.Google Scholar
- 9.L. Ngo and P. Haddawy. Probabilistic logic programming and Bayesian networks. Proceedings from Asian computing science conference LNCS 1023, pages 286–300, 1995.Google Scholar
- 12.S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, New Jersey, 1995.Google Scholar