An empirical analysis of the complexity of model-based diagnosis

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

We empirically study the computational complexity of diagnosing systems with real-world structure. We adopt the structure specified by a small-world network, which is a graphical structure that is common to a wide variety of naturally-occurring systems, ranging from biological systems, the WWW, to human-designed mechanical systems. We randomly generate a suite of digital circuit models with small-world network structure, and show that diagnosing these models is computationally hard.

Original languageEnglish
Title of host publicationECAI 2006
Subtitle of host publication17th European Conference on Artificial Intelligence August 29 - September 1, 2006, Riva del Garda, Italy
EditorsGerhard Brewka, Silvia Coradeschi, Anna Perini, Paolo Traverso
PublisherIOS Press BV
Pages783-784
Number of pages2
ISBN (Print)9781586036423
Publication statusPublished - 2006

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume141
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

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