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 | English |
|---|---|
| Pages | 793-799 |
| Number of pages | 7 |
| State | Published - 2012 |
| Externally published | Yes |
| 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 |
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.
| Funders | Funder number |
|---|---|
| Frankel Center for Computer Science at Ben-Gurion University |