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.
|Title of host publication||Conference on Uncertainty in Artificial Intelligence (UAI)|
|Place of Publication||Banff|
|Number of pages||8|
|Publication status||Published - 2004|
Bibliographical note(top 10%, plenary presentation, double-blind peer reviewed by >3 reviewers)
Cozman, F. G., de Campos, C. P., Ide, J. S., & da Rocha, J. C. F. (2004). Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments. In Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 104-111). AUAI Press. http://www.eeecs.qub.ac.uk/~c.decampos/publist/papers/decampos2004c.pdf