Advertisement

A Constraint-Based Approach to the Description of Competence

  • S. White
  • D. Sleeman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1621)

Abstract

A competency description of a software component seeks to describe what the artefact can and cannot do. We focus on a particular kind of competence, called fitness-for-purpose, which specifies whether running a software component with a supplied set of inputs can satisfy a given goal. In particular, we wish to assess whether a chosen problem solver, together with one or more knowledge bases, can satisfy a given (problem solving) goal. In general, this is an intractable problem. We have therefore introduced an effective, practical, approximation to fitness-for-purpose based on the plausibility of the goal. We believe that constraint (logic) programming provides a natural approach to the implementation of such approximations. We took the Common LISP constraints library SCREAMER and extended its symbolic capabilities to suit our purposes. Additionally, we formulated an example of fitness-for-purpose modelling using this enhanced library.

Keywords

Knowledge Base Knowledge Acquisition Problem Solver Novice User Meal Preparation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arcos, J. L., Plaza, E., (1994), “Integration of Learning into a Knowledge Modelling Framework”, in Proceedings of the Eighth European Knowledge Acquisition Workshop (EKAW’ 94), LNCS, Springer Verlag.Google Scholar
  2. 2.
    Arcos, J. L., Plaza, E., (1997), “Noos: An Integrated Framework for Problem Solving and Learning”, Research Report 97-02, Institut d’Investigació en Intelligència Artificial (IIIA), Barcelona, Spain.Google Scholar
  3. 3.
    Benjamins, V. R., Plaza, E., Motta, E., Fensel, D., Studer, R., Wielinga, B., Schreiber, G., Zdrahal, Z., Decker, S., (1998), “IBROW3 — An Intelligent Brokering Service for Knowledge Component Reuse on the World-Wide Web”, in proceedings of the Eleventh Banff Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW98), Banff, Alberta, Canada.Google Scholar
  4. 4.
    Benjamins, V. R., Wielinga, B., Wielemaker, J., Fensel, D., (1999), “Brokering Problem Solving Knowledge on the Internet”, in the proceedings of the Eleventh European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW’ 99), LNCS, Springer Verlag.Google Scholar
  5. 5.
    Collins, A., Michalski, R. S., (1989), “The Logic of Plausible Reasoning: A Core Theory”, Cognitive Science, Vol. 13, pp. 1–49.CrossRefGoogle Scholar
  6. 6.
    Fensel, D., Schönegge, A., (1997), “Using KIV to Specify and Verify Architectures of Knowledge-Based Systems”, in Proceedings of the Twelfth International Conference on Automated Software Engineering (ASEC-97), Incline Village, Nevada.Google Scholar
  7. 7.
    Fensel, D., Schönegge, A., (1998), “Inverse Verification of Problem Solving Methods”, International Journal of Human-Computer Studies, Vol. 49,No. 4, pp. 339–361.zbMATHCrossRefGoogle Scholar
  8. 8.
    Gennari, J. H., Cheng, H., Altman, R. B., Musen, M. A., (1998), “Reuse, CORBA, and Knowledge-based Systems”, International Journal of Human-Computer Studies, Vol. 49,No. 4, pp. 523–546.CrossRefGoogle Scholar
  9. 9.
    Giunchiglia, F., Walsh, T., (1992), “A Theory of Abstraction”, Artificial Intelligence, Vol. 56,No. 2–3, pp. 323–390.CrossRefMathSciNetGoogle Scholar
  10. 10.
    Graner, N., Sleeman, D., (1993), “MUSKRAT: A Multistrategy Knowledge Refinement and Acquisition Toolbox”, in proceedings of the Second International Workshop on Multistrategy Learning, R. S. Michalski and G. Tecuci (Eds.), pp. 107–119.Google Scholar
  11. 11.
    Imielinski, T., (1987), “Domain Abstraction and Limited Reasoning”, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 997–1003.Google Scholar
  12. 12.
    Johnson, J., (1997), “Mathematics, Representation, and Problem Solving”, Mathematics Today (Bulletin of the Institute of Mathematics and its Applications), Vol. 33,No. 3., pp. 78–80.zbMATHGoogle Scholar
  13. 13.
    O’Hara, K., Shadbolt, N., (1996), “The Thin End of the Wedge: Efficiency and the Generalised Directive Model Methodology”, in Shadbolt, N., O’Hara, K., Schreiber, G., (Eds), Advances in Knowledge Acquisition, proceedings of the 9th European Knowledge Acquisition Workshop (EKAW’ 96), Nottingham, UK, pp. 33–47.Google Scholar
  14. 14.
    O’Hara, K., Shadbolt, N., van Heijst, (1998), “Generalised Directive Models: Integrating Model Development and Knowledge Acquisition”, International Journal of Human-Computer Studies, Vol. 49,No. 4, pp. 497–522.CrossRefGoogle Scholar
  15. 15.
    Morik, K., Wrobel, S., Kietz J-U., Emde, W., (1993), “Knowledge Acquisition and Machine Learning: Theory, Methods and Applications”, Academic Press, London.Google Scholar
  16. 16.
    Motta, E., O’Hara, K., Shadbolt, N., (1996), “Solving VT in VITAL: A Study in Model Construction and Knowledge Reuse”, International Journal of Human-Computer Studies, Vol. 44,No. 3, pp. 333–371.CrossRefGoogle Scholar
  17. 17.
    Oroumchian, F., (1995), “Theory of Plausible Reasoning”, in Information Retrieval by Plausible Inferences: An Application of the Theory of Plausible Reasoning of Collins and Michalski, PhD Thesis, School of Computer and Information Science, Syracuse University, New York.Google Scholar
  18. 18.
    Pierret-Golbreich, C., (1998), “Supporting Organization and Use of Problem-solving Methods Libraries by a Formal Approach”, International Journal of Human-Computer Studies, Vol. 49,No. 4, pp. 471–495.CrossRefGoogle Scholar
  19. 19.
    Polya, G., (1957), “How To Solve It: A New Aspect of Mathematical Method”, Doubleday Anchor Books, New York.Google Scholar
  20. 20.
    Puppe, F., (1998), “Knowledge Reuse among Diagnostic Problem-Solving Methods in the Shell-Kit D3”, International Journal of Human-Computer Studies, Academic Press, Vol. 49,No. 4, pp. 627–649.CrossRefGoogle Scholar
  21. 21.
    Simonis, H., (1995), “The CHIP System and Its Applications”, in Montanari, U., Rossi, F., (Eds.), Principles and Practice of Constraint Programming, proceedings of the First International Conference on the Principles and Practice of Constraint Programming, Lecture Notes in Computer Science Series, Springer Verlag, pp. 643–646.Google Scholar
  22. 22.
    J. M. Siskind, D. A. McAllester, (1993), “SCREAMER: A Portable Efficient Implementation of Nondeterministic Common LISP’”, Technical Report IRCS-93-03, University of Pennsylvania Institute for Research in Cognitive Science.Google Scholar
  23. 23.
    J. M. Siskind, D. A. McAllester, (1993), “Nondeterministic LISP as a Substrate for Constraint Logic Programming”, in proceedings of AAAI-93.Google Scholar
  24. 24.
    Sleeman, D., White, S., (1997), “A Toolbox for Goal-driven Knowledge Acquisition”, in proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, (COGSCI’ 97), Stanford, CA.Google Scholar
  25. 25.
    Turing, A. M., (1937), “On Computable Numbers, with an Application to the Entscheidungsproblem”, in Proceedings of the London Mathematical Society, Vol. 42(ii), pp. 230–265; correction Vol. 43, pp. 544–546.CrossRefGoogle Scholar
  26. 26.
    Wallace M. G., Novello, S. and Schimpf, J., (1997) “ECLIPSE: A Platform for Constraint Logic Programming”, ICL Systems Journal, Vol 12,Issue 1, May 1997.Google Scholar
  27. 27.
    White, S., Sleeman, D., (1998), “Providing Advice on the Acquisition and Reuse of Knowledge Bases in Problem Solving”, in proceedings of the Eleventh Banff Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW98), Banff, Alberta, Canada.Google Scholar
  28. 28.
    White, S., Sleeman, D., (1998), “Constraint Handling in Common LISP”, Technical Report AUCS/TR9805, Department of Computing Science, University of Aberdeen, Scotland, UK.Google Scholar
  29. 29.
    White, S., (forthcoming), “Enhancing Knowledge Acquisition with Constraint Technology”, PhD Thesis, Department of Computing Science, University of Aberdeen, Scotland, UK.Google Scholar
  30. 30.
    Wielinga, B. J., Akkermans, J. M., Schreiber A. Th., (1998), “A Competence Theory Approach to Problem Solving Method Construction”, International Journal of Human-Computer Studies, Vol. 49,No. 4.Google Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • S. White
    • 1
  • D. Sleeman
    • 1
  1. 1.Department of Computing ScienceKing’s College, University of AberdeenAberdeenUK

Personalised recommendations