Intelligent Systems: The Weakest Link?

  • David Davenport
Conference paper
Part of the NATO ASI Series book series (volume 114)


This paper investigates the role intelligent systems may play in the design, operation and maintenance of safety critical applications. It questions whether the techniques currently used to construct intelligent knowledge-based systems can produce designs which can meet the performance requirements of such applications and which, in particular, can be trusted. The paper concludes that conventional rule-based systems are essentially ad-hoc and thus not really suitable, however, more sophisticated techniques as embodied in the abductive, qualitative reasoning and inscriptor approaches are seen to point the way to a solution.


Intelligent systems rule-based systems safety abductive reasoning qualitative reasoning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Benefits and Risks of Knowledge-Based Systems, Working party report of the Society for Science and Society, Oxford University Press, 1989Google Scholar
  2. 2.
    Davenport D., Expert Systems: Should we trust them?, Bilkent University Technical report CIS9209, 1992Google Scholar
  3. 3.
    Astrom K.J., Anton J.J. & Arzen K.E., Expert Control. In Automatica Vol.22 No.3 p277–286, Pergamon Journals, 1986CrossRefGoogle Scholar
  4. 4.
    Michie D., Machine Executable Skills from Silent Brains. In Addis & Muir(eds) Research and Development in Expert Systems VII;.Google Scholar
  5. 5.
    Chamiak E. & McDermott D., An Introduction to Artificial Intelligence, Addison-Wesley; 1985Google Scholar
  6. 6.
    Peter S. Sell, Expert Systems-A Practical Introduction; Macmillan Pub. 1985.Google Scholar
  7. 7.
    Krause P., O’Niel M. and Glowinski A., Can we Formally Specify a Medical Decision Support System? In Proc. European Workshop on the Verification and Validation of KBS, Logica, 1991Google Scholar
  8. 8.
    Peng Y. & Reggia J.A., Abductive Inference Models for Diagnostic Problem-Solving, Springer-Verlag, 1990Google Scholar
  9. 9.
    Cohn A.G., Approaches to Qualitative Reasoning, AI Review Vol.3 No’s 2&3, Blackwell Scientific Publications, 1989Google Scholar
  10. 10.
    (eds) Weld D.S. & de Kleer J., Readings in Qualitative Reasoning about Physical Systems, Morgan Kaufman, 1990Google Scholar
  11. 11.
    Pearce D., A Model Based Approach to Validation. In EUROVAV91 Logica, 1991Google Scholar
  12. 12.
    Davis R., Diagnostic Reasoning Based on Structure and Behaviour. In Qualitative Reasoning about Physical Systems; (eds) Bobrow D.G., Elsevier 1984, MTT Press 1985-6Google Scholar
  13. 13.
    Meystel A., New Control Solutions Based on Multi-Resolutional Architectures. In Proc. NATO ARW on Intelligent Systems: Safety, Reliability and Maintainability Issues, Kusadasi, Izmir, Turkey, August 1992Google Scholar
  14. 14.
    Davenport D., Knowledge Representation: Building Solid Foundations, Bilkent University Technical report CIS9203, 1992Google Scholar
  15. 15.
    Davenport D., Sen M. and Erturk A., Knowall: An Experimental Inscriptor-based Expert System Shell. In Proc. of 1st. Turk Yapay Zeka ve Yapay Sinir Aglar Sempozyumu, 25–26 July 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • David Davenport
    • 1
  1. 1.Computer Eng. & Info. Sciences Dept.Bilkent UniversityAnkaraTurkey

Personalised recommendations