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International Conference on Knowledge Engineering and Knowledge Management

EKAW 1992: Current Developments in Knowledge Acquisition — EKAW '92 pp 318–336Cite as

Causal model-based knowledge acquisition tools: Discussion of experiments

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 599))

Abstract

The aim of this paper is to study causal knowledge and demonstrate how it can be used to support the knowledge acquisition process. The discussion is based on three experiments we have been involved in. First, two classes of Causal Model-Based Knowledge Acquisition Tools are identified: bottom-up designed causal models and top-down designed causal models. The properties of each type of tool and how they contribute to the whole knowledge acquisition process is then discuted.

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References

  1. Nathalie Aussenac, Jean-Paul Krivine, and Jean Sallantin. Introduction to the special issue on knowledge acquisition. Revue d'Intelligence Artificielle, 1992. (To appear).

    Google Scholar 

  2. Joost A. Breuker and Bob J. Wielinga. Models of expertise in knowledge acquisition. In G. Guida and C. Tasso, editors, Topics in Expert System Design: Methodologies and Tools. North Holland Publishing Company, The Netherlands, 1989.

    Google Scholar 

  3. B. Chandrasekaran. Towards a functional architecture for intelligence based on generic information processing tasks. In Proceedings of the 10 th International Joint Conference on Artificial Intelligence, pages 1183–1192, Milan, Italy, 1987.

    Google Scholar 

  4. Jean Charlet. ACTE: a strategic knowledge acquisition method. In Proceedings of the 5 th European Workshop on Knowledge Acquisition for Knowledge-Based Systems, Glasgow, UK, 1991. Springer Verlag. (To appear).

    Google Scholar 

  5. Jean Charlet. ACTE: une méthode d'acquisition des connaissances pour un système de diagnostic. Revue d'Intelligence Artificielle, 1992. (To appear).

    Google Scholar 

  6. Jean Charlet and Olivier Gascuel. Knowledge acquisition by causal model and meta-knowledge. In John H. Boose, Brian R. Gaines, and Jean G. ganascia, editors, Proceedings of the 3 th European Workshop on Knowledge Acquisition for Knowledge-Based Systems, pages 212–225, Paris, France, July 1989.

    Google Scholar 

  7. W. J. Clancey. Heuristic Classification. Artificial Intelligence, 27(3):289–350, 1985.

    Article  Google Scholar 

  8. Luca Console, Mauro Fossa, and Pietro Torasso. Acquisition of causal knowledge in the CHECK system. Computers and Artificial Intelligence, 8(4):323–345, 1989.

    Google Scholar 

  9. Luca Console and Pietro Torasso. A logical approach to deal with incomplete causal models in diagnostic problem solving. In Lecture Notes in Computer Science, 313. Springer-Verlag, 1988.

    Google Scholar 

  10. Luca Console and Pietro Torasso. Hypothetical reasoning in causal models. International Journal of Intelligent Systems, 5, 1990.

    Google Scholar 

  11. Marie-Odile Cordier and Chantal Reynaud. Knowledge acquisition techniques and second generation expert systems. Applied Artificial Intelligence, 5(3):209–226, 1991.

    Google Scholar 

  12. Jean-Marc David and Jean-Paul Krivine. Augmenting experience-based diagnosis with causal reasoning. Applied Artificial Intelligence, 3(2–3):239–248, 1989.

    Google Scholar 

  13. Jean-Marc David and Jean-Paul Krivine. Designing knowledge-based systems within functional architecture: the DIVA experiment. In Proceedings of the 5 th IEEE Conference on Artificial Intelligence Applications, Miami, Florida, USA, March 1989.

    Google Scholar 

  14. Randall Davis. Form and content in model based reasoning. In Proceedings of the IJCAI workshop on Model-Based reasoning, 1989.

    Google Scholar 

  15. Larry Eshelman. MOLE: a knowledge acquisition tool for cover-and-differentiate systems. In Sandra Marcus, editor, Automating Knowledge Acquisition for Expert Systems. Kluwer Academic Publishers, 1988.

    Google Scholar 

  16. Thomas Gruber. Learning why by being told what. IEEE-Expert, pages 65–75, 1991.

    Google Scholar 

  17. Franck R. Hickman, Jonathan L. Killin, Lise Land, Tim Mulhall, David Porter, and Robert M. Taylor. Analysis for Knowledge-Based Systems, a Practical Guide to the KADS Methodology. Ellis Horwood, 1989.

    Google Scholar 

  18. P. N. Johnson-Laird. Mental models in cognitive science. Cognitive Science, 4:71–115, 1980.

    Article  Google Scholar 

  19. Garry Kahn, S. Nowlan, and John McDermott. MORE: an intelligent knowledge acquisition tool. In A. Joshi, editor, Proceedings of the 9 th International Joint Conference on Artificial Intelligence, pages 581–585, Los Angeles, CA, August 18–23 1985. M. Kaufmann, Inc.

    Google Scholar 

  20. Werner Karbach, Marc Linster, and Angi Voss. Models, methods, roles and tasks: many labels — one idea? Knowledge Acquisition, 2:279–299, 1990.

    Google Scholar 

  21. Werner Karbach, Angi Voss, Ralf Schuckey, and Uwe Drouven. Model-K: Prototyping at the knowledge level. In D. Herin-Aimé, R. Dieng, J.-P. Regouard, and J.P. Angoujard, editors, Proceedings of the 1 th conference on Knowledge Modeling & Expertise Transfer, pages 195–208. IOS Press, 1991.

    Google Scholar 

  22. W. Long, S. Naimi, M. G. Criscitiello, S. G. Pauker, and P. Szolovits. An aid to physiological reasoning in the management of cardiovascular disease. In Proceedings of the Computers in Cardiology Conference, 1984.

    Google Scholar 

  23. William Long. Medical diagnosis using a probabilistic causal network. Applied Artificial Intelligence, 3(2–3):367–383, 1989.

    Google Scholar 

  24. Sandra Marcus and John McDermott. SALT: a knowledge acquisition language for propose-and-revise systems. Artificial Intelligence, 39(1):1–37, 1989.

    Article  Google Scholar 

  25. John McDermott. Preliminary steps towards a taxonomy of problem-solving methods. In Sandra Marcus, editor, Automating Knowledge Acquisition for Expert Systems. Kluwer Academic Publishers, 1988.

    Google Scholar 

  26. Enrico Motta, Tim Rajan, and Marc Eisenstadt. Knowledge acquisition as a process of model refinement. Knowledge Acquisition, 2:21–49, 1990.

    Google Scholar 

  27. Mark A. Musen. Conceptual models of interactive knowledge acquisition tools. Knowledge Acquisition, 1:73–88, 1989.

    Google Scholar 

  28. R. Neches, W.R. Swartout, and J. Moore. Explainable (and maintainable) expert systems. In A. Joshi, editor, Proceedings of the 9 th International Joint Conference on Artificial Intelligence, pages 382–389, Los Angeles, CA, August 18–23 1985. M. Kaufmann, Inc.

    Google Scholar 

  29. M. Reinders, E. Vinkhuyzen, A. Voss, H. Akkermans, J. Balder, B. Bartsch-Spörl, B. Bredeweg, U. Drouven, F. van Harmelen, W. Karbach, Z. Karssen, G. Schreiber, and B. Wielinga. A conceptual modelling framework for knowledge-level reflection. AI Communications, 4(2/3), 1991.

    Google Scholar 

  30. Jon Sticklen, B. Chandrasekaran, and W. E. Bond. Distributed causal reasoning for knowledge acquisition: A functional approach to device understanding. In Proceedings of the 3 rd Banff Workshop for Knowledge Acquisition, Banff, Canada, 1988. (revised version: Applied AI 3(2–3):275–304,1989).

    Google Scholar 

  31. W.R. Swartout. Xplain: A system for creating and explaining expert consulting programs. Artificial Intelligence, 21(3):285–325, 1983.

    Google Scholar 

  32. P. Szolovits, Ramesh S. Patil, and W. B. Schwartz. Artificial intelligence in medical diagnostic. Annals of Internal Medicine, 108:80–87, 1988.

    PubMed  Google Scholar 

  33. B. J. Wielinga and J. A. Breuker. KADS: Structured knowledge acquisition for expert systems. In Proceedings of the 5 th International Workshop Expert Systems & their Applications, Avignon, France, 1985. Gerfau.

    Google Scholar 

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Author information

Authors and Affiliations

  1. DIAM, INSERM U194 & Service d'Informatique Médicale de l'AP-HP, 91, Bd de l'Hôpital, 75634, Paris Cedex 13, France

    Jean Charlet

  2. E.D.F. Direction des Etudes et Recherches, 1, Av. du Général de Gaulle, 92141, Clamart Cedex, France

    Jean-Paul Krivine

  3. Laboratoire de Recherche en Informatique, Bât. 490, Université Paris-Sud, 91405, Orsay Cedex, France

    Chantal Reynaud

Authors
  1. Jean Charlet
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  2. Jean-Paul Krivine
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  3. Chantal Reynaud
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Editor information

Thomas Wetter Klaus-Dieter Althoff John Boose Brian R. Gaines Marc Linster Franz Schmalhofer

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© 1992 Springer-Verlag Berlin Heidelberg

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Charlet, J., Krivine, JP., Reynaud, C. (1992). Causal model-based knowledge acquisition tools: Discussion of experiments. In: Wetter, T., Althoff, KD., Boose, J., Gaines, B.R., Linster, M., Schmalhofer, F. (eds) Current Developments in Knowledge Acquisition — EKAW '92. EKAW 1992. Lecture Notes in Computer Science, vol 599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55546-3_48

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  • DOI: https://doi.org/10.1007/3-540-55546-3_48

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55546-9

  • Online ISBN: 978-3-540-47203-2

  • eBook Packages: Springer Book Archive

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