Compiling Model-Based Diagnosis to Boolean Satisfaction

Authors

  • Amit Metodi Ben-Gurion University
  • Roni Stern Ben-Gurion University
  • Meir Kalech Ben-Gurion University
  • Mike Codish Ben-Gurion University

DOI:

https://doi.org/10.1609/aaai.v26i1.8222

Keywords:

model-based diagnosis, SAT

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.

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Published

2021-09-20

How to Cite

Metodi, A., Stern, R., Kalech, M., & Codish, M. (2021). Compiling Model-Based Diagnosis to Boolean Satisfaction. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 793–799. https://doi.org/10.1609/aaai.v26i1.8222

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning