Probabilistic logic with strong independence

Authors:
Fabio G. Cozman;Cassio P. de Campos;José Carlos F. da Rocha
Affiliations:
Escola Politécnica, Univ. de São Paulo, São Paulo, SP, Brazil;Escola Politécnica, Univ. de São Paulo, São Paulo, SP, Brazil;Univ. Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

This papers investigates the manipulation of statements of strong independence in probabilistic logic. Inference methods based on polynomial programming are presented for strong independence, both for unconditional and conditional cases. We also consider graph-theoretic representations, where each node in a graph is associated with a Boolean variable and edges carry a Markov condition. The resulting model generalizes Bayesian networks, allowing probabilistic assessments and logical constraints to be mixed.