Heuristic control knowledge

Problem Solving Models Comparison of Approaches
Part of the Lecture Notes in Computer Science book series (LNCS, volume 723)


In the prospect of acquiring control knowledge, we have concentrated on the relation between reasoning knowledge and domain knowledge. In this perspective, the central entity we need to analyse is the control roles that appear in the description of a problem solving method. After describing what these control roles are in central approaches such as KADS and Commet, we advocate a refinement of these models consisting in defining a heuristic level of description for heuristic control knowledge. We define this level of description, characterise its relation with problem solving methods, and finally show a small example of the use of this type of knowledge with the Sisyphus problem.


Domain Knowledge Knowledge Acquisition Reasoning Process Case Model Reasoning Level 
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. [Albert & Vogel, 90]
    Albert, P. et Vogel, C.,“K-Station, un environnement intégré pour le génie cognitif”, Génie Logiciel et Systèmes Experts, No. 19, Juin 1990.Google Scholar
  2. [Anjewierden et al., 90]
    Anjewierden, A., Wielmaker, J., et Toussaint, C., “Shelley — Computer Aided Knowledge Engineering”. In B. Wielinga et al. (Eds.), Current Trends in Knowledge Acquisition, IOS Press, 1990, pp. 41–59.Google Scholar
  3. [Brown & Chandrasekaran 89]
    David C. Brown and B. Chandrasekaran “Design Problem Solving: Knowledge Structures and Control Strategies”, Research Notes in Artificial Intelligence, Pitman Publishing, London, 1989.Google Scholar
  4. [Bradshaw & Boose 89]
    Bradshaw, J.M. and Boose, J.H. “Decision Analysis Techniques for Knowledge Acquisition: Combining Information and Preferences using Aquinas”. in International Journal of Man-Machine Studies, 1989.Google Scholar
  5. [Bylander & Chandrasekaran, 87]
    Bylander, T. and Chandrasekaran, B., “Generic Tasks for Knowledge-Based Reasoning: The “Right” Level of Abstraction for Knowledge Acquisition”, Int. J. Man-Machine Studies, Vol. 26, 1987, pp. 231–243.Google Scholar
  6. [Causse & al., 92]
    Causse, K., Cañamero, D. and Gobinet, P. “La généricité en acquisition des connaissances”, Actes des Journées d'Acquisition des Connaissances (JAC), pp 161–176, Dourdan, Avril 1992.Google Scholar
  7. [Chandrasekaran, 86]
    Chandrasekaran, B., “Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design”, IEEE Expert, Automne 1986, pp. 23–30.Google Scholar
  8. [Clancey 85]
    William J. Clancey “Heuristic Classification”, in Artificial Intelligence, Vol 27, pp 289–350, 1985.Google Scholar
  9. [Clancey 89]
    William J. Clancey “The Knowledge Level Reinterpreted: Modeling How Systems Interact”, in Machine Learning, Vol 4, pp 285–291, 1989.Google Scholar
  10. [Clancey 92]
    William J. Clancey “The Knowledge Level Reinterpreted: Modeling Socio-Technical Systems”, in Working Notes of the AAAI Spring Symposium “Cognitive Aspects of Knowledge Acquisition”, pp 47–56, 1March 1992.Google Scholar
  11. [Eshelman, 88]
    Eshelman, L., “MOLE: A Knowledge-Acquisition Tool for Cove4-More-0r-and-Differentiate Systems”. In S. Marcus (Ed.) Automating Knowledge Acquisition for Expert Systems, Kluwer Academic, 1988.Google Scholar
  12. [Gruber 89]
    Thomas R. Gruber, “The Acquistion of Strategic Knowledge”, Academic Press Inc, San Diego, 1989.Google Scholar
  13. [Jonckers & al 92]
    Jonckers, V., Geldof, S. and De Vroede, K. “The COMMET methodology and workbench”, VUB AI Memo 92-8, 1992.Google Scholar
  14. [Karbach et al., 91]
    Karbach, W., Linster, M., and Voß, A., “Models of Problem-Solving-One label-One Idea?”. In B. Wielinga et al. (Eds.), Current Trends in Knowledge Acquisition, IOS Press, 1990, pp. 173–189.Google Scholar
  15. [Linster & Musen 91]
    Linster, M. and Musen, M. “Using KADS to Build KONCOCIN: A Conceptual Model of ONCOCIN”, in Proceedings of BANFF91.Google Scholar
  16. [Linster 92]
    Linster, M. Ed., “Sisyphus '92; Models of problem solving”, GMD Arbeitspapiere 630, Mars 1992.Google Scholar
  17. [Marcus, 88]
    Marcus, S., “SALT: A Knowledge-Acquisition Tool for Propose-and-Revise Systems”. In S. Marcus (Ed.) Automating Knowledge Acquisition for Expert Systems, Kluwer Academic, 1988.Google Scholar
  18. [McDermott, 88]
    McDermott, J., “Preliminary Steps Toward a Taxonomy of Problem-Solving Methods”. In S. Marcus (Ed.), Automating Knowledge Acquisition for Expert Systems, Kluwer Academic, 1988, pp. 225–266.Google Scholar
  19. [Musen, 89]
    Musen, M.A., Automated Generation of Mode4—More—01-Based Knowledge-Acquisition Tools, Pitman Publishing, London, 1989.Google Scholar
  20. [Newell, 82]
    Newell, A., “The Knowledge Level”, Artificial Intelligence, Vol. 19, No. 2, 1982, pp. 87–127.Google Scholar
  21. [Shadbolt & Wielinga 90]
    Shadbolt, N. and Wielinga, B., “Knowledge based knowledge acquisition: the next generation of support tools”. In B. Wielinga et al. (Eds.), Current Trends in Knowledge Acquisition, IOS Press, 1990, pp. 313–338.Google Scholar
  22. [Steels, 90]
    Steels, L., “Components of Expertise”, AI Magazine, Eté 1990, pp. 28–49.Google Scholar
  23. [Steels, 92]
    Steels, L., “Reusability and configuration of applications by nonprogrammers”, VUB AI Memo 92-4, april 1992.Google Scholar
  24. [Stefik 81]
    Mark Stefik “Planning and Meta-Planning (MOLGEN: Part 2)”, in Artificial Intelligence, Vol. 16, pp. 141–170, 1981.Google Scholar
  25. [Tu et al., 91]
    Tu, S., Shahar, Y., Dawes, J., Winkles, J., Puerta, A. and Musen, M., “A Problem-Solving Model for Episodic Skeletel-Plan Refinement”, Proceedings of the 6th. AAAI-KAW, Banff, Canada, 1991.Google Scholar
  26. [Wielinga et al., 91]
    Wielinga, B.J., Shreiber, A.T. and Breuker, J.A., “KADS: A Modelling Approach to Knowledge Engineering”, Knowledge Acquisition, 1992.Google Scholar
  27. [Wielinga et al., 92]
    Wielinga, B.J., van de Velde, W., Shreiber, G. and Akkermans, H., “The CommonKADS Framework for Knowledge Modellin4—More— 0g”, In Proceedings of Banff'92.Google Scholar
  28. [Woodward 91]
    J. Brian Woodward, “Developing K-ONCOCIN: A Case Study in the Cognitive Process of Knowledge Engineers”. In Proceedings of BANFF'91.Google Scholar
  29. [Woodward 92]
    J. Brian Woodward, “Cognition-Based Tools for Knowledge Acquisition”, in Proceedings AAAI Spring Syposium “Cognitive Aspects of Knowledge Acquisition”, Stanford 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  1. 1.Laboratoire de Recherche en InformatiqueUniversité de Par4-More-ois Sud CNRS U.A. 410OrsayFrance

Personalised recommendations