MODEL-K for prototyping and strategic reasoning at the knowledge level

  • Werner Karbach
  • Angi Voß

Abstract

To close the gap between knowledge level and symbol level, the MODEL-K language allows to specify KADS conceptual models and to refine them to operational systems. Since both activities may be arbitrarily interleaved, early prototyping is supported at the highest level. Systems written in MODEL-K contain their conceptual model, making them more transparent, easier to communicate to the expert, to explain to the user, and to maintain by the knowledge engineer.

The strategy layer of KADS is supposed to control and possibly repair the activities being modeled by the lower layers. MODEL-K views this kind of strategic reasoning as a meta-activity. In the REFLECT project, we came to view meta-activities like resource-management or competence assessment as ordinary problem solving methods, that in turn can be described using KADS. Correspondingly, we extended MODEL-K to model and operationalize such meta-activities. In particular, the lower three layers and the system they model are automatically kept consistent due to the construction of MODEL-K1.

Keywords

Neral Dition Bran Neomycin Systen 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    B. Bartsch-Spoerl, B. Bredeweg, C. Coulon, U. Drouven, F. van Harmelen, W. Karbach, M. Reinders, E. Vinkhuyzen, and A. Voß. Studies and experiments with reflective problem solvers. ESPRIT Basic Research Action P3178 REFLECT, Report IR.3.1,2 RFL/BSR-UvA/II.2/1, REFLECT Consortium, August 1991.Google Scholar
  2. 2.
    B. Bartsch-Sporl, M. Reinders, H. Akkermans, B. Bredeweg, T. Christaller, U. Drouven, F. van Harmelen, W. Karbach, G. Schreiber, A. Voß, and B. Wielinga. A tentative framework for knowledge-level reflection. ESPRIT Basic Research Action P3178 REFLECT, Deliverable IR.2 RFL/BSR-ECN/I.3/1, BSR Consulting and Netherlands Energy Research Foundation ECN, August 1990.Google Scholar
  3. 3.
    C. Beierle, W. Olthoff, and A. Voß. Towards a formalization of the software development process. In Software engineering 86, IEE Computing Series 6, London, 1986. PeterPeregrinus Ltd.Google Scholar
  4. 4.
    J. A. Breuker and B. J. Wielinga. Model Driven Knowledge Acquisition. In P. Guida and G. Tasso, editors, Topics in the Design of Expert Systems, pages 265–296, Amsterdam, 1989. North Holland.Google Scholar
  5. 5.
    B. Chandrasekaran. Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design. IEEE Expert, 1(4):279–299, 1986.MathSciNetGoogle Scholar
  6. 6.
    B. Chandrasekaran. Generic tasks as building blocks for knowledge-based systems: The diagnosis and routine design examples. The Knowledge Engineering Review, 3(3):183–210, 1988.CrossRefGoogle Scholar
  7. 7.
    D.C. Brownand B. Chandrasekaran. Design Problem Solving: Knowledge Structures and Control Strategies. Research Notes in Artificial Intelligence. Pitman, London, 1989.Google Scholar
  8. 8.
    Th. Christaller, F. di Primio, and A. Voß. The AI Workbench BABYLON: An Open and Portable Development Environment for Expert Systems. Academic Press, London, 1992.MATHGoogle Scholar
  9. 9.
    W.J. Clancey. From Guidon to Neomycin and Heracles in twenty short lessons. The AI Magazine, 7(3):40–61, August 1986.Google Scholar
  10. 10.
    R. Davis. Applications of meta-knowledge to the construction, maintenance, and use of large knowledge-bases. AI memo 283, Stanford University, Palo Alto, July 1976.Google Scholar
  11. 11.
    R. Davis and B. G. Buchanan. Meta-level knowledge: Overview and applications. In IJCAI-77, pages 920–927, Cambridge MA, August 1977.Google Scholar
  12. 12.
    D. Fensel, J. Angele, and D. Landes. KARL:: A knowledge acquisition and representation language. In J.C. Rault, editor, Proceedings of the 11th International Conference Expert systems and their applications, volume 1 (Tools, Techniques & Methods), pages 513–528, Avignon, 1991. EC2.Google Scholar
  13. 13.
    Ch. Floyd. A systematc look at prototyping. In R. Budde, K. Kuhlenkamp, L. Mathiassen, and H. Züllighoven, editors, Approaches to Prototyping, pages 1–18. Elsevier, Berlin, 1984.CrossRefGoogle Scholar
  14. 14.
    T. R. Gruber. The acquisition of strategic knowledge, volume 4 of Perspectives in artificial intelligence. Academic Press, Boston, 1989.Google Scholar
  15. 15.
    E. Hudlicka and V.R. Lesser. Meta-level control through fault detection and diagnosis. In Proceedings of the National Conference on Artificial Intelligence, pages 153–1161, Austin, Texas, 1984.Google Scholar
  16. 16.
    W. Jansweijer. PDP. PhD thesis, University of Amsterdam, 1988.Google Scholar
  17. 17.
    W. Karbach, M. Linster, and A. Voß. Models, methods, roles and tasks: many labels — one idea? Knowledge Acquisition journal, 2:279–299, 1990.CrossRefGoogle Scholar
  18. 18.
    Marc Linster. Linking modeling to make sense and modeling to implement systems in an operational environment. In Thomas Wetter, Klaus-Dieter Althoff, John Boose, Brian Gaines, Marc Linster, and Franz Schmalhofer, editors, Current developments in knowledge acquisition: EKAW92, LNAI, Heidelberg, 1992. Springer.Google Scholar
  19. 19.
    P. Maes. Computational reflection. Technical report 87-2, Free University of Brussels, AI Lab, 1987.Google Scholar
  20. 20.
    S. Marcus, editor. Automatic knowledge acquisition for expert systems. Kluwer, 1988.Google Scholar
  21. 21.
    M.A. Musen. Automated Generation of Model-Based Know ledge-Acquisition Tools. Pitman, London, 1989. Research Notes in Artificial Intelligence.Google Scholar
  22. 22.
    R. Neches, W. Swartout, and J. Moore. Explainable (and maintainable) expert systems. In IJCAI-85, Los Angeles, 1985.Google Scholar
  23. 23.
    A. Newell. The knowledge level. Artificial Intelligence, 1982:82–127, 1982.Google Scholar
  24. 24.
    D. Perlis. Languages with self-reference I: Foundations. Artificial Intelligence, 25:301–322, 1985.MathSciNetMATHCrossRefGoogle Scholar
  25. 25.
    F. Puppe. Med2: How domain characteristics induce expert system features. In H. Stoyan, editor, GWAI-85, pages 272–284. Springer-Verlag, 1986.Google Scholar
  26. 26.
    S.J. Russell and S. Zilberstein. Composing real-time systems. In Proceedings of the 12th International Joint Conference on Artificial Intelligence, Sydney, Australia, volume 1, pages 212–217, San Mateo, 1991. Morgan Kaufmann.Google Scholar
  27. 27.
    G. Schreiber, B. Bartsch-Sporl, B. Bredeweg, F. van Harmelen, W. Karbach, M. Reinders, E. Vinkhuyzen, and A. Voß. Designing architectures for knowledge-level reflection. ESPRIT Basic Research Action P3178 REFLECT, Deliverable IR.4 RFL/UvA/III.1/4, REFLECT Consortium, August 1991.Google Scholar
  28. 28.
    H. E. Shrobe. Dependency directed reasoning in the analysis of programs which modify complex data structures. In IJCAI-79, pages 829–835, Tokio, 1979.Google Scholar
  29. 29.
    B. Smith. Reflection and semantics in a procedural language. Technical Report TR-272, MIT, Computer Science Lab., Cambridge, Massachussetts, 1982. Also in: Readings in Knowledge Representation, Brachman, R.J. and Levesque, H.J. (eds.), Morgan Kaufman, California, 1985, pp. 31-40.Google Scholar
  30. 30.
    M. Stefik. Planning and meta-planning (molgen: Part 2). AI journal, 16:141–170, 1981.Google Scholar
  31. 31.
    G. J. Sussman. A Computer Model of Skill Acquisition, volume 1 of Artificial Intelligence Series. American Elsevier, New York, 1975.Google Scholar
  32. 32.
    F. van Harmelen. Meta-level Inference Systems. Research Notes in AI. Pitmann, Morgan Kaufmann, London, San Mateo California, 1991.Google Scholar
  33. 33.
    F. van Harmelen and J. Balder. (ML) 2: A formal language for kads models of expertise. Knowledge Acquisition, 4, Special Issue in KADS(1):127–159, 1992.CrossRefGoogle Scholar
  34. 34.
    J. Vanwelkenhuysen and P. Rademakers. Mapping knowledge-level analysis onto a computational framework. In L. Aiello, editor, Proceedings ECAI’90, Stockholm, pages 681–686, London, 1990. Pitman.Google Scholar
  35. 35.
    A. Voß, W. Karbach, B. Bartsch-Spoerl, and B. Bredeweg. Reflection and competent problem solving. In Th. Christaller, editor, GWAI-91, 15th German Workshop on Artificial Intelligence, pages 206–215, London, 1991. Springer Verlag.Google Scholar
  36. 36.
    A. Voß, W. Karbach, C.H. Coulon, U. Drouven, and B. Bartsch-Spoerl. Generic specialists in competent behavior. In Proceedings of ECAI-92, 1992.Google Scholar
  37. 37.
    A. Voß, W. Karbach, U. Drouven, and D. Lorek. Competence assessment in configuration tasks. In L.C. Aiello, editor, Proceedings of the 9th European Conference on Artificial Intelligence, pages 676–681, London, 1990. ECCAI, Pitman.Google Scholar
  38. 38.
    T. Wetter. First-order logic foundation of the KADS conceptual model. In B. Wielinga, J. Boose, B. Gaines, G. Schreiber, and M. van Someren, editors, Current trends in knowledge acquisition, pages 356–375, Amsterdam, May 1990. IOS Press.Google Scholar
  39. 39.
    R. Weyhrauch. Prolegomena to a theory of mechanized formal reasoning. Artificial Intelligence, 13, 1980. Also in: Readings in Artificial Intelligence, Webber, B.L. and Nilsson, N.J. (eds.), Tioga publishing, Palo Alto, CA, 1981, pp. 173–191. Also in: Readings in Knowledge Representation, Brachman, R.J. and Levesque, H.J. (eds.), Morgan Kaufman, California, 1985, pp. 309–328.Google Scholar
  40. 40.
    B. Wielinga and J. Breuker. Models of expertise. In Proceedings ECAI’86, pages 306–318, Brighton, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Werner Karbach
    • 1
  • Angi Voß
    • 1
  1. 1.AI Research DivisionGerman National Research Institute for Computer-Science (GMD)Sankt AugustinGermany

Personalised recommendations