The route to success: a performance comparison of diagnosis algorithms

  • Authors:

  • Iulia Nica;Ingo Pill;Thomas Quaritsch;Franz Wotawa

  • Affiliations:

  • Institute for Software Technology, Graz University of Technology, Graz, Austria;Institute for Software Technology, Graz University of Technology, Graz, Austria;Institute for Software Technology, Graz University of Technology, Graz, Austria;Institute for Software Technology, Graz University of Technology, Graz, Austria

  • Venue:
  • IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
  • Year:
  • 2013

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Abstract

Diagnosis, i.e., the identification of root causes for failing or unexpected system behavior, is an important task in practice. Within the last three decades, many different AI-based solutions for solving the diagnosis problem have been presented and have been gaining in attraction. This leaves us with the question of which algorithm to prefer in a certain situation. In this paper we contribute to answering this question. In particular, we compare two classes of diagnosis algorithms. One class exploits conflicts in their search, i.e., sets of system components whose correct behavior contradicts given observations. The other class ignores conflicts and derives diagnoses from observations and the underlying model directly. In our study we use different reasoning engines ranging from an optimized Horn-clause theorem prover to general SAT and constraint solvers. Thus we also address the question whether publicly available general reasoning engines can be used for an efficient diagnosis.