---
abstract: 'This paper examines and compares several different approaches to design of intelligent systems for diagnosis applications. These include expert systems (or knowledge based systems), truth (or reason) maintenance systems, case based reasoning systems, and inductive approaches like decision trees, neural networks (or connectionist systems), and statistical pattern classification systems. Each of these approaches is demonstrated through the design of a system for a simple automobile fault diagnosis task. The paper also discusses the domain characteristics that influence the choice of a specific technique (or combination of techniques) for a given application.'
altloc:
  - http://www.cs.iastate.edu/~honavar/Papers/diagnosis.ps
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creators_name:
  - family: Balakrishnan
    given: Karthik
    honourific: ''
    lineage: ''
  - family: Honavar
    given: Vasant
    honourific: ''
    lineage: ''
date: 1998
date_type: published
datestamp: 2002-09-21
department: ~
dir: disk0/00/00/24/69
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eprint_status: archive
eprintid: 2469
fileinfo: /style/images/fileicons/application_pdf.png;/2469/1/diagnosis.pdf
full_text_status: public
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keywords: 'Intelligent diagnosis, expert systems, neural networks, case-based reasoning, model-based systems, decision trees, knowledge acquisition'
lastmod: 2011-03-11 08:55:00
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metadata_visibility: show
note: ~
number: 3/4
pagerange: 239-290
pubdom: FALSE
publication: Journal of Intelligent Systems
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refereed: TRUE
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rev_number: 12
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status_changed: 2007-09-12 16:44:59
subjects:
  - comp-sci-mach-learn
  - comp-sci-neural-nets
  - comp-sci-art-intel
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title: Intelligent Diagnosis Systems
type: journalp
userid: 3444
volume: 8
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