Abstract
In order to combine both the contribution of conceptual models to help knowledge acquisition and the contribution of second generation expert systems to build problem solvers that are less brittle and easier to explain, we propose an approach to operationalize conceptual models. This approach is based upon the shell AIDE which allows the knowledge engineer to model at a high level of abstraction. The shell is based upon a mechanism of translation to code automatically the completely formalized conceptual model, in a lower level model directly implemented. The link between the conceptual model and the KBS is thus preserved. In addition to the advantages bound to prototyping at the knowledge level, the AIDE's approach allows validation and explaination at this same high level of abstraction.
This research is partially supported by the French Ministry of Research and Technology under the PRC-IA project, and the French agency ANVAR under the programme “recherche exploratoire 90”.
Preview
Unable to display preview. Download preview PDF.
References
Aikins, J.S. “Prototypical Knowledge for Expert Systems.” In Artificial Intelligence, 20, (1983), pp. 163–210.
Boulitreau-Lefèvre, P. “Validating at the knowledge level: an application to the project AIDE-SATIN.” DEA report, University of Technology of Compiègne, September 1991 (in french).
Bourcier, F. “An interface between the projects DOCAL and AIDE-SATIN using conceptual graphs.” DEA report, University of Technology of Compiègne, Septembre 1991 (in french).
Breuker, J. & Wielinga, B. “Models of Expertise in Knowledge Acquisition.” In Topics in Expert System Design, Methodologies and Tools, North-Holland, Guida and al (Eds.), 1989, pp. 265–295.
Chandrasekaran, B. “Towards a Functional Architecture for Intelligence Based on Generic Information Processing Tasks.” In Proceedings of the IJCAI '87, pp. 1183–1192.
Chandrasekaran, B. “Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples.” In the Knowledge Engineering Review, 1988, 3(3), pp. 183–210.
Clancey, W.J. & Letsinger, R. “NEOMYCIN: Reconfiguring a Rule-Based Expert System for Application to Teaching.” In Proceedings of the IJCAI '81, pp. 829–836.
Clancey, W.J. “The Advantages of Abstract Control Knowledge.” In Proceedings of the AAAI '83, pp. 74–78.
Clancey, W.J. “Heuristic Classification.” In Artificial Intelligence, 27, (1985), pp. 289–350.
Clancey, W.J. “From GUIDON to NEOMYCIN and HERACLES in Twenty Short Lessons: ORN Final Report 1979–1985.“, AI Magazine, 7(3), (1986), pp. 40–60.
Clancey, W.J. “Viewing Knowledge Bases as Qualitative Models.” In IEEE Expert, summer 1989, pp. 9–23.
Clancey, W.J. “Model construction operators.” In Artificial Intelligence 53 (1992), pp 1–115.
Console, L. & Torasso, P. “Heuristic and causal reasoning in check.” In proceedings of the 12th world congress IMACS 88, Paris, july 1988, vol. 4, pp. 283–286.
David, J.M. & Krivine, J.P. “Explaining Reasoning from Knowledge Level Models.” In Proceedings of the ECAI '90, pp. 186–188.
Fensel, D., Angele, J. & Landes, D. “KARL: a Knowledge Acquisition and Representation Language.“ In Proceedings of the Eleventh International Conference “Expert Systems and their Applications”, Avignon, May 1991, pp. 513–525.
Gloess, P. “Contribution to the optimization of mechanisms of reasoning in knowledge representation structures.” Doctorat d'Etat Thesis, University of Technology of Compiègne, January 1990 (in french).
Gloess, P. “U-LOG, an Ordered Sorted Logic with Typed Attributes.“ In Proceedings of the Third International Conference on Programming Language Implementation and Logic Programming, pp. 275–286; Lectures Notes in Computer Science n∘ 528, Springer Verlag, Passau, august 1991.
Gréboval, M.H. “Modeling an explicative reasoning: an application to the projet AIDE-SATIN.” DEA report, University of Technology of Compiègne, September 1991 (in french).
Hasling, D.W., Clancey, W.J. & Rennels, G.R. “Strategic explanations for a diagnostic consultation system.” In the International Journal of Man-Machine Studies20(1), (1984), pp. 3–19.
Hayes-Roth, B., Hewett, M., Vaughan Johnson, M. & Garvey, A. “ACCORD; a framework for a class of design tasks.” Report N∘ 88-19, Knowledge Systems Laboratory, Stanford University, Stanford, CA (1988)
Iwasaki, Y., Keller, R. & Feigenbaum, Ed. “Generic Tasks or wide-ranging knowledge bases ?” In the Knowledge Engineering Review, 3(3), (1988), pp. 215–216.
Karbach, W., Voß, A., Schuckey, R., & Drouven, U. “MODEL-K: Prototyping at the Knowledge Level.” In Proceedings of the Eleventh International Conference “The Expert Systems and their Applications”, Avignon, May 1991, pp. 501–511.
Kassel, G. & Gréboval, C. “The project AIDE: first rapport.” Report HEUDIASYC N∘ 91/46/DI, University of Technology of Compiègne, September 91 (in french).
Kassel, G. “The principle of knowledge level reflection: a unifying principle in the project AIDE.” Report HEUDIASYC, University of Technology of Compiègne. To appear in April 1992.
Newell, A. “The Knowledge Level.” In Artificial Intelligence 18 (1982), pp. 87–127.
Nicaud, J.F. & Saïdi, M. “Explanation in the solving of algebraic exercises.” In the french Review in Artificial Intelligence, Eds. Hermès, 4(2) (1990), pp. 125–148 (in french).
Reinders, M., Vinkhuyzen, E., Voß, A., Akkermans, H., Balder, J., Bartsch-Spörl, B., Bredeweg, B., Drouven, U., Harmelen, F., Karbach, W., Karssen, Z., Schreiber, G. & B. Wielinga “A Conceptual Modelling Framework for Knowledge-Level Reflection.” In AI Communications 4 (2/3) (1991), 49–128.
Schreiber, A.Th., Wielinga, B.J. & Breuker, J.A. “The KADS Framework for Modelling Expertise.” In Pre-Proceedings of the EKAW '91.
Smith, J.W., Svirbely, J.R., Evans, C.A., Straum, P., Josephson, J.R. & Tanner, M.C. “RED: A red-cell antibody identification expert module.” In the Journal of Medical Systems 9 (3), (1985), pp. 121–138.
Sowa, J. “Conceptual structures: information processing in mind and machine.” Addison wesley, Reading Mass.
Steels, L. “The deepening of expert systems.” In proceedings of the 12th world congress IMACS 88, Paris, july 1988, vol. 4, pp. 323–326.
Steels, L. “Components of expertise.” In the AI Magazine 11 (2), summer 1990, pp. 28–49.
Sticklen, J. “Problem-solving architecture at the knowledge level.” In the Journal of Experimental and Theoretical Artificial Intelligence, 1(4), (1989), pp. 233–247.
Vanwelkenhuysen, J., Rademakers, P. “Mapping a Knowledge Level Analysis onto a Computational Framework.” In Proceedings of the ECAI '90, London:Pitman, pp. 661–666.
Van Marcke, K.”KRS: an Object Oriented Representation Language.” In the french Review in Artificial, Eds. Hermès,(4) (1987),.
Weiss, M., Kulikowski, C.A., Amarel S. & Safir, A. “A Model-Based Method for Computer-Aided Medical decision-Making.” In Artificial Intelligence, 11, (1978), pp.145–172.
Wetter, T. “First order logic foundations of the KADS conceptual model.” In Current Trends in Knowledge Acquisition, B. Wielinga et al (eds.), IOS Press, Amsterdam, (1990), pp. 356–375.
Wielinga, B.J. & Breuker, J.A. “Models of Expertise.” In Proceedings of the ECAI '86, pp. 307–318.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Greboval, C., Kassel, G. (1992). An approach to operationalize conceptual models: The shell AIDE. 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_33
Download citation
DOI: https://doi.org/10.1007/3-540-55546-3_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55546-9
Online ISBN: 978-3-540-47203-2
eBook Packages: Springer Book Archive