Probabilistic logic with strong independence

Fabio G. Cozman, Cassio P. de Campos, José Carlos F. da Rocha

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - IBERAMIA-SBIA 2006 - 2nd International Joint Conference, 10th Ibero-American Conference on AI, 18th Brazilian AI Symposium, Proceedings
EditorsJaime Simão Sichman, Helder Coelho, Solange Oliveira Rezende
PublisherSpringer
Pages612-621
Number of pages10
ISBN (Electronic)978-3-540-45464-9
ISBN (Print)3540454624, 978-3-540-45462-5
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventIBERAMIA-SBIA 2006 - 2nd International Joint Conference, 10th Ibero-American Conference on AI, 18th Brazilian AI Symposium - Ribeirao Preto, Brazil
Duration: 23 Oct 200627 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4140 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceIBERAMIA-SBIA 2006 - 2nd International Joint Conference, 10th Ibero-American Conference on AI, 18th Brazilian AI Symposium
CountryBrazil
CityRibeirao Preto
Period23/10/0627/10/06

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