A comparative analysis of Horn models and Bayesian Networks for diagnosis

Luigi Portinale, Pietro Torasso

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

Abstract

The aim of the paper is to formally relate logical Horn models and Bayesian Networks (BNs) in the framework of diagnostic reasoning. This is pursued by pointing out similarities between the two formalisms at the modeling level and by introducing into BNs a suitable notion of derivation. We also discuss modeling issues underlying the choice of Horn-based models vs BNs, by making explicit the “completion semantics” underlying a BN. This correspondence between “completed” Horn theories and BNs allows us to formally justify classical diagnostic schemata adopted for BNs.

Original languageEnglish
Title of host publicationAI*IA 97
Subtitle of host publicationAdvances in Artificial Intelligence - 5th Congress of the Italian Association for Artificial Intelligence, Proceedings
EditorsMaurizio Lenzerini
PublisherSpringer Verlag
Pages254-265
Number of pages12
ISBN (Print)3540635769, 9783540635765
DOIs
Publication statusPublished - 1997
Externally publishedYes
Event5th Congress of the Italian Association for Artificial Intelligence, AI*IA 1997 - Rome, Italy
Duration: 17 Sept 199719 Sept 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1321
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Congress of the Italian Association for Artificial Intelligence, AI*IA 1997
Country/TerritoryItaly
CityRome
Period17/09/9719/09/97

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