Compiling Model-Based Diagnosis to Boolean Satisfaction

Amit Metodi, Roni Stern, Meir Kalech, Michael Codish

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

Abstract

This paper introduces an encoding of Model Based Diagnosis (MBD) to Boolean Satisfaction (SAT) focusing on minimal cardinality diagnosis. The encoding is based on a combination of sophisticated MBD preprocessing algorithms and SAT compilation techniques which together provide concise CNF formula. Experimental evidence indicates that our approach is superior to all published algorithms for minimal cardinality MBD. In particular, we can determine, for the first time, minimal cardinality diagnoses for the entire standard ISCAS-85 benchmark. Our results open the way to improve the state-of-the-art on a range of similar MBD problems.

Original languageEnglish
Pages793-799
Number of pages7
StatePublished - 2012
Externally publishedYes
Event26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada
Duration: 22 Jul 201226 Jul 2012

Conference

Conference26th AAAI Conference on Artificial Intelligence, AAAI 2012
Country/TerritoryCanada
CityToronto
Period22/07/1226/07/12

Bibliographical note

Publisher Copyright:
Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

The first and last authors acknowledge the support of the Frankel Center for Computer Science at Ben-Gurion University.

FundersFunder number
Frankel Center for Computer Science at Ben-Gurion University

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