Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments
This work addresses the challenge of flexible probabilistic reasoning in AI, particularly for domains requiring handling of uncertainty and relational data, though it appears incremental as it builds on existing credal network theory.
The paper tackles the problem of representing and reasoning with propositional and first-order constructs under various probabilistic assessments (precise, imprecise, indeterminate, qualitative) by developing a representation language based on credal networks. It presents new exact and approximate inference algorithms using multilinear programming and interval probability propagation, demonstrating empirically superior performance compared to existing methods.
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.