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Artificial Intelligence Review

, Volume 3, Issue 2–3, pp 129–158 | Cite as

Abductive reasoning in multiple fault diagnosis

  • T. Finin
  • G. Morris
Article

Abstract

Abductive reasoning involves generating an explanation for a given set of observations about the world. Abduction provides a good reasoning framework for many AI problems, including diagnosis, plan recognition and learning. This paper focuses on the use of abductive reasoning in diagnostic systems in which there may be more than one underlying cause for the observed symptoms. In exploring this topic, we will review and compare several different approaches, including Binary Choice Bayesian, Sequential Bayesian, Causal Model Based Abduction, Parsimonious Set Covering, and the use of First Order Logic. Throughout the paper we will use as an example a simple diagnostic problem involving automotive troubleshooting.

Keywords

Neural Network Artificial Intelligence Complex System Nonlinear Dynamics Good Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Intellect Ltd. 1989

Authors and Affiliations

  • T. Finin
    • 1
  • G. Morris
    • 2
  1. 1.Paoli Research CenterUnisysPaoliUSA
  2. 2.AI LaboratoryInternal Revenue ServiceWashington, DCUSA

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