An Empirical Analysis of the Complexity of Model-Based Diagnosis

Authors:
Gregory Provan
Affiliations:
Computer Science Department, University College Cork, Cork, Ireland, email: g.provan@cs.ucc.ie
Venue:
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Year:
2006

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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.