Compiling Model-Based Diagnosis to Boolean Satisfaction

Amit Metodi; Roni Stern; Meir Kalech; Michael Codish

Compiling Model-Based Diagnosis to Boolean Satisfaction

Amit Metodi, Roni Stern, Meir Kalech, Michael Codish

Research output: Contribution to conference › Paper › peer-review

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 language	American English
Pages	793-799
Number of pages	7
State	Published - 1 Jan 2012
Event	26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada Duration: 22 Jul 2012 → 26 Jul 2012

Conference

Conference	26th AAAI Conference on Artificial Intelligence, AAAI 2012
Country/Territory	Canada
City	Toronto
Period	22/07/12 → 26/07/12

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Cite this

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title = "Compiling Model-Based Diagnosis to Boolean Satisfaction",

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.",

author = "Amit Metodi and Roni Stern and Meir Kalech and Michael Codish",

note = "Publisher Copyright: Copyright {\textcopyright} 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 26th AAAI Conference on Artificial Intelligence, AAAI 2012 ; Conference date: 22-07-2012 Through 26-07-2012",

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AU - Stern, Roni

AU - Kalech, Meir

AU - Codish, Michael

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N2 - 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.

AB - 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.

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M3 - Paper

SP - 793

EP - 799

T2 - 26th AAAI Conference on Artificial Intelligence, AAAI 2012

Y2 - 22 July 2012 through 26 July 2012

ER -

Compiling Model-Based Diagnosis to Boolean Satisfaction

Abstract

Conference

All Science Journal Classification (ASJC) codes

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