On Problems with the Knowledge Level Perspective
Conference paper
Abstract
In this paper some points of criticism on Newell’s Knowledge Level Hypothesis are investigated. Among those are: the inability to represent control, the potential computational inadequacy, the lack of predictive power and the non-operational character (the problem of ‘how to build it’). We discuss Sticklen’s Knowledge Level Architecture Hypothesis in which he tries to overcome these problems. On the basis of general arguments as well as specific insights from our KADS knowledge level modelling approach we reject the points of criticism. We also argue that the extension Sticklen proposes is not necessary and partly also unwanted.
Keywords
Knowledge Acquisition Knowledge Level Knowledge Type Symbol Level Computational Tractability
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References
- Akkermans, H., van Harmelen, F., Schreiber, G., and Wielinga, B. (1990). A formalisation of knowledge-level models for knowledge acquistion. International Journal of Intelligent Systems. Forthcoming.Google Scholar
- Alexander, J., Freiling, M., Shulman, S., Rehfuss, S., and Messick, S. (1988). Ontological analysis: an ongoing experiment. In Boose, J. and Gaines, B., editors, Knowledge-Based Systems, Volume 2: Knowledge Acquisition Tools for Expert Systems, pages 25–37. Academic Press, London.Google Scholar
- Breuker, J., Wielinga, B., van Someren, M., de Hoog, R., Schreiber, G., de Greef, P., Bredeweg, B., Wielemaker, J., Billault, J.-P., Davoodi, M., and Hayward, S. (1987). Model Driven Knowledge Acquisition: Interpretation Models. ESPRIT Project P1098 Deliverable D1 (task A1), University of Amsterdam and STL Ltd,.Google Scholar
- Breuker, J. A. and Wielinga, B. J. (1989). Model Driven Knowledge Acquisition. In Guida, P. and Tasso, G., editors, Topics in the Design of Expert Systems, pages 265–296, Amsterdam. North Holland.Google Scholar
- Bylander, T. and Chandrasekaran, B. (1988). Generic tasks in knowledge-based reasoning: The ‘right’ level of abstraction for knowledge acquisition. In Gaines, B. and J. Boose, editors, Knowledge Acquisition for Knowledge Based Systems, volume 1, pages 65–77. Academic Press, London.Google Scholar
- Clancey, W. (1983). The epistemology of a rule based system-a framework for explanation. Artificial Intelligence, 20:215–251. Also: Stanford Heuristic Programming Project, Memo HPP-81-17, November 1981, also numbered STAN_CS-81-896.CrossRefGoogle Scholar
- Clancey, W. (1985a). Acquiring, representing and evaluating a competence model of diagnostic strategy. In Chi, Glaser, and Far, editors, Contributions to the Nature of Expertise.Google Scholar
- Clancey, W. (1985b). Heuristic classification. Artificial Intelligence, 27:289–350.CrossRefGoogle Scholar
- Eshelman, L., Ehret, D., McDermott, J., and Tan, M. (1988). MOLE: a tenacious knowledge acquisition tool. In Boose, J. and Gaines, B., editors, Knowledge Based Systems, Volume 2: Knowledge Acquisition Tools for Expert Systems, pages 95–108, London. Academic Press.Google Scholar
- Gruber, T. (1989). The Acquisition of Strategic Knowledge. Perspectives in Artificial Intelligence, Volume 4. Academic Press, San Diego.Google Scholar
- McDermott, J. (1989). Preliminary steps towards a taxonomy of problem-solving methods. In Marcus, S., editor, Automating Knowledge Acquisition for Expert Systems, pages 225–255. Kluwer Academic Publishers, The Netherlands.Google Scholar
- Musen, M., Fagan, L., Combs, D., and Shortliffe, E. (1988). Use of a domain model to drive an interactive knowledge editing tool. In Boose, J. and Gaines, B., editors, Knowledge-Based Systems, Volume 2: Knowledge Acquisition Tools for Expert Systems, pages 257–273, London. Academic Press.Google Scholar
- Newell, A. (1982). The knowledge level. Artificial Intelligence, 1982:87–127.CrossRefGoogle Scholar
- Patii, R. (1988). Artificial intelligence techniques for diagnostic reasoning in medicine. In Shobe, H. and AAAI, editors, Exploring Artificial Intelligence: Survey Talks from the National Conferences on Artificial Intelligence, pages 347–379. Morgan Kaufmann, San Mateo, California.Google Scholar
- Pylyshyn, Z. (1986). Computation and Cognition: Toward a Foundation for Cognitive Science. MIT Press, Cambridge, USA.Google Scholar
- Schank, R. (1975). Conceptual information processing. North Holland, Amsterdam.MATHGoogle Scholar
- Schreiber, G., Breuker, J., Bredeweg, B., and Wielinga, B. (1988). Modelling in KBS development. In Proc. 2th European Knowledge Acquisition Workshop, Bonn, pages 7.1–7.15, St. Augustin. GMD. GMD-Studien 143.Google Scholar
- Schreiber, G., Wielinga, B., Hesketh, P., and Lewis, A. (1989). A KADS design description language. ESPRIT Project 1098, deliverable B7 UvA-B7-PR-007, University of Amsterdam.Google Scholar
- Steels, L. (1988). Components of expertise. AI Memo 88-16, AI Lab, Vrije Univer-siteit Brussel. Published in AI Magazine, summer 1990.Google Scholar
- Sticklen, J. (1989). Problem solving architecture at the knowledge level. Journal of Experimental and Theoretical Artificial Intelligence.Google Scholar
- Wielinga, B., Akkermans, H., Schreiber, G., and Balder, J. (1989). A knowledge acquisition perspective on knowledge-level models. In Boose, J. H. and Gaines, B. R., editors, Proceedings Knowledge Acquisition Workshop KAW’89, Banff, pages 36-1–36-22, University of Calgary. SRDG Publications.Google Scholar
- Wielinga, B., Boose, J., Gaines, B., Schreiber, A., and van Someren, M., editors (1990). Current Trends in Knowledge Acquisition. IOS, Amsterdam.Google Scholar
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