Uncertainty in Artificial Intelligence
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Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments
Fabio Cozman, Cassio de Campos, Jaime Ide, Jose Ferreira da Rocha
Abstract:
This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.
Keywords: null
Pages: 104-111
PS Link:
PDF Link: /papers/04/p104-cozman.pdf
BibTex:
@INPROCEEDINGS{Cozman04,
AUTHOR = "Fabio Cozman and Cassio de Campos and Jaime Ide and Jose Ferreira da Rocha",
TITLE = "Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "104--111"
}


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