Applying neural computing to expert system design: Coping with complex sensory data and attribute selection

  • H. Tirri
New Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 367)


Recently the relation of subsymbolic (“neural computing”) and symbolic computing has been a topic of intense discussion. Our purpose is to focus this discussion to the particular application area of expert system design. We address some of the drawbacks of current expert systems and study the possibility of using neural computing methodologies to improve their competence. The topic can be discussed at various levels of integration: the higher the integration level, the more symbolic functionalities (such as an inference engine) are implemented directly at the level of the neural computational model.

In this paper we address the lowest levels of integration: neural networks that can be used to implement feature recognizers which allow symbolic inference engines to make direct use of complex sensory input via so called detector predicates. We also introduce the notion of self organization as a means to determine those attributes (properties) of data that reflect meaningful statistical relationships in the expert system input space, thus addressing the difficult problem of conceptual clustering (“abstraction”) of information. The concepts introduced are illustrated by two examples: an automatic inspection system for circuit packs and an expert system for respiratory and anesthesia monitoring. The adopted approach differs considerably from the earlier research on the use of neural networks as expert systems, where the only method to obtain knowledge is learning from training data. In our approach the synergy of rules and detector predicates combines the advantages of both worlds: it maintains the clarity of the rule-based knowledge representations at the higher reasoning levels without sacrificing the power of noise-tolerant pattern association (“inference by memory”) offered by neural computing methods.


Expert System Neural Computing Attribute Selection Computer Vision System Input Instance 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [AnRo 88]
    Anderson,J.A. and E.Rosenfeld (Eds.), Neurocomputing. Foundations of research. The MIT Press, 1988.Google Scholar
  2. [BePe 87]
    Becker, L.E. and J. Peng, Using activation networks for analogical ordering of consideration: one method for integrating connectionistic and symbolic processing. IEEE International Conference on Neural Networks, San Diego, pp.367–371, 1987.Google Scholar
  3. [BLMW 88]
    Bounds, D.G, P.J. Lloyd, B. Matthew and G. Waddell, A multi layer perceptron network for the diagnosis of low back pain. IEEE International Conference Neural Networks, San Diego, pp.481–489, 1988.Google Scholar
  4. [CaWK 84]
    Callero, M., D.A. Waterman and J. Kipps, TATR: a prototype expert system for tactical air targeting. Rand Report R-3096-ARPA, The Rand Corporation, Santa Monica, CA, 1984.Google Scholar
  5. [Davi 84]
    Davis,R., Diagnostic reasoning based on structure and behaviour. Artificial Intelligence (24), 347–410, 1984.Google Scholar
  6. [DuHa 73]
    Duda,R.O. and P.E.Hart, Pattern classification and scene analysis, John wiley & Sons, 1973.Google Scholar
  7. [DuSh 88]
    Dutta, S. and S. Shekhar, Bond rating: a non-conservative application of neural networks. IEEE International Conference on Neural Networks, San Diego, pp.443–450, 1988.Google Scholar
  8. [Gall 88]
    Gallant,S.I., Connectionistic expert systems. Communications of the ACM (31), pp. 152–169, 1988.Google Scholar
  9. [Gross 88]
    Grossberg,S.(Ed.), Neural networks and natural intelligence. The MIT Press, 1988.Google Scholar
  10. [Hech 87a]
    Hecht-Nielsen,R., Neurocomputer applications. In R.Eckmiller and C.v.d.Malsburg (Eds.), Neural computers, Springer-Verlag, pp. 445–451, 1987.Google Scholar
  11. [Hech 87b]
    Hecht-Nielsen,R., Counterpropagation networks. To appear in Applied Optics, 1987.Google Scholar
  12. [KaSc 85]
    Kandel,E.R. and J.H.Schwartz, Principles of neural science, Elsevier, 1985.Google Scholar
  13. [KeJo 86]
    Keravnou,E.T. and L.Johnson, competent expert systems — a case study in fault diagnosis. McGraw-Hill, 1986.Google Scholar
  14. [Koho 88]
    Kohonen,T., Self-organization and associative memory. 2nd Edition. Springer-Verlag, 1988.Google Scholar
  15. [Lipp 87]
    Lippmann,R.P., An introduction to computing with neural nets. IEEE ASSP Magazine, pp. 4–22, 1987.Google Scholar
  16. [MoRT 89a]
    Morris,R.J.T.,L.Rubin and H.Tirri, A computer vision system for font orientation detection: solution by optimal detection and learning vector quantization approaches. In preparation, preprint available from authors, 1989.Google Scholar
  17. [MoRT 89b]
    Morris,R.J.T.,L.Rubin and H.Tirri, A comparison of feedforward and self-organizing approaches to the font orientation problem. To appear in IJCNN'89, 1989.Google Scholar
  18. [Nels 82]
    Nelson, W.R., REACTOR: an expert system for diagnosis and treatment of nuclear reactor accidents. Proceedings of AAAI, 1982.Google Scholar
  19. [Quin 79]
    Quinlan,R., Discovering rules from large collections of examples. A case study. In D.Mitchie (ed.), Expert systems in the microelectronic age. Edinburgh University Press, 1979.Google Scholar
  20. [RaCM 87]
    Rader, C.D., V.M. Crowe and B.G. Marcot, CAPS: a pattern recognition expert system prototype for respiratory and anesthesia monitoring. Proceedings of Western Conference on Expert Systems, Anaheim, CA, pp. 162–168, 1987.Google Scholar
  21. [RuMc 86]
    Rumelhart,D.E. and J.L. McClelland (Eds.), Parallel distributed processing: explorations in the microstructures of cognition. Vol 1,2. The MIT Press, 1986.Google Scholar
  22. [WaSh 82]
    Wallis, J.W. and E.H. Shortliffe,Explanatory power for medical expert systems: studies in the representation of causal relationships for clinical consultations. Meth. Inform. Med (21), pp.127–136, 1982.Google Scholar
  23. [Wate 86]
    Waterman,D.A., A guide to expert systems. Addison-Wesley, 1986.Google Scholar
  24. [WeKu 84]
    Weiss, S.M. and C.A. Kulikowski, A practical guide to designing expert systems. Rowman&allanheld (NJ,USA), 1984.Google Scholar
  25. [Werb 74]
    Werbos,P., Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard U. Committee on applied Mathematics, 1974.Google Scholar
  26. [Werb 88]
    Werbos, P., Backpropagation: past and future. IEEE International Conference on Neural Networks, San Diego, pp.343–353, 1988.Google Scholar
  27. [Zade 83]
    Zadeh, L.A., The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems (11), pp. 199–227, 1983.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • H. Tirri
    • 1
  1. 1.Department of Computer ScienceUniversity of HelsinkiFinland

Personalised recommendations