WebShell: The development of web based expert systems

  • Andrew Stranieri
  • John Zeleznikow


It is rare to find knowledge based appearing on the World Wide Web. This is, in part, due to difficulties associated with porting expert system shell environments to the world wide web. For instance, many real world applications require shell environments that do more than make mere inferences with rules. However, integrating rule based reasoning with other approaches exacerbate the difficulties in placing hybrid, ystems on the World Wide Web. In this paper, we present a knowledge based system shell, WebShell that enables knowledge based systems to be developed and executed on the World Wide Web. WebShell models knowledge using two distinct techniques; decision trees for procedural type tasks and argument trees for tasks that are more complex, ambiguous or uncertain. Rather than translate decision tree knowledge into rules, we map the decision trees into sets we call sequenced transition networks. These sets can readily be stored in a relational database format in a way that simplifies the inference engine design. The inference engines for both the argument tree and the decision tree models execute on the server side and have been implemented using a very small (30K) PHP program.


Decision Tree Expert System Data Item Lookup Table Procedural Knowledge 
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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  1. 1.
    Ball, W. J. 1994. Using Virgil to analyse public policy arguments: a system based on Toulmin’s informal logic. Social Science Computer Review. Volume 12 Issue 1 pp. 26–37. SpringCrossRefGoogle Scholar
  2. 2.
    Clark, P. 1991. A Model of Argumentation and Its Application in a Cooperative Expert System. PhD thesis. Turing Institute. Department of Computer Science. University of Strathclyde. Glasgow.Google Scholar
  3. 3.
    Dick, J. P. 1991. A conceptual, case-relation representation of text for intelligent retrieval. Ph.D Thesis. University of Toronto. 1991. Canada.Google Scholar
  4. 4.
    Dick, J. P. 1987. Conceptual retrieval and case law. Proceedings of the First International Conference on Artificial Intelligence and Law. Boston. May 27–29. ACM Press. p106–115.Google Scholar
  5. 5.
    Fox, J., and Parsons, S., 1998. Arguing about Beliefs and Actions. In Hunter, A., and Parsons, S., (Eds). 1998. Applications of Uncertainty Formalisms. Springer. Berlin. Pp 266–302.Google Scholar
  6. 6.
    Grove, R, F., and Hulse, A, C., 1999. An Internet based expert system for reptile identification. PAJava99. Proceedings of the First International Conference on the Practical Application of Jaye. Practical Application Company Ltd. pp 165–73.Google Scholar
  7. 7.
    Huntington, D. 2000. Web-based expert system are on the way: Java bsed Web delivery. PCAI Intelligent Solutions for Desktop Computers vol 14, no 6. Nov-Dec, pp34–36Google Scholar
  8. 8.
    Koers, A. W., Kracht, D., Smith, M., Smits, J. M. and Weusten, M. C. M. 1989. Knowledge Based Systems in Law. Computer/Law Series. Kluwer. The Netherlands.Google Scholar
  9. 9.
    Krause, P., Ambler, S., Elvang-Goransson., and Fox, J., 1995. A Logic of Argumentation for Reasoning under Uncertainty. Computational Intelligence. Volume 11, Number 1. pp113–131MathSciNetCrossRefGoogle Scholar
  10. 10.
    Marshall, C. C, 1989. Representing the structure of legal argument. Proceedings of Second International Conference on Artificial Intelligence and Law. ACM Press,USA. pp121–127Google Scholar
  11. 11.
    Shortliffe, E. H. 1976. Computer based medical consultations: MYCIN. New York: Elsevier.Google Scholar
  12. 12.
    Stranieri, A., Zeleznikow, J., Yearwood, J., 2002. Argumentation structures that integrate dialectical and monoletical reasoning. To appear in Knowledge Engineering ReviewGoogle Scholar
  13. 13.
    Toulmin S. 1958. The Uses of Argument. Cambridge University Press.CambridgeGoogle Scholar
  14. 14.
    Zeleznikow, J. and Stranieri, A. 1998. Split-Up: An intelligent decision support system which provides advice upon property division following divorce. Journal of Law and Information Technology: 6(2): 190–213. Oxford University Press.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Andrew Stranieri
    • 1
  • John Zeleznikow
    • 2
  1. 1.Donald Berman Laboratory for Information Technology and LawLa Trobe UniversityBundooraAustralia
  2. 2.Centre for Forensic Statistics and Legal Reasoning, Faculty of LawUniversity of EdinburghScotlandUK

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