Differentiating problem solving methods
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Problem solving methods (PSM's) are important in constructing modular and reusable knowledge-based systems, as they specify the different types of knowledge used in knowledge-based reasoning, as well as under what circumstances what knowledge is to be applied. We argue that the formal modeling of PSM's is a useful means for clarifying, communicating and comparing problem-solving knowledge. This paper shows how such PSM's can be formally defined. We illustrate this by developing a formal model for the Cover- and-Differentiate method for diagnosis, and comparing this to Heuristic Classification.
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- Akkermans, H., van Harmelen, F., Schreiber, G., & Wielinga, B. (1991). A formalisation of knowledge-level models for knowledge acquistion. International Journal of Intelligent Systems. forthcoming.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., & Hayward, S. (1987). Model Driven Knowledge Acquisition: Interpretation Models. ESPRIT Project P1098 Deliverable D1 (task A1), University of Amsterdam and STL Ltd.Google Scholar
- Eshelman, L. (1988). MOLE: A knowledge-acquisition tool for cover-and-differentiate systems. In Marcus, S., editor, Automating Knowledge Acquisition for Expert Systems, pages 37–80. Kluwer Academic Publishers, The Netherlands.Google Scholar
- Eshelman, L., Ehret, D., McDermott, J., & Tan, M. (1988). MOLE: a tenacious knowledge acquisition tool. In Boose, J. & Gaines, B., editors, Knowledge Based Systems, Volume 2: Knowledge Acquisition Tools for Expert Systems, pages 95–108, London. Academic Press.Google Scholar
- Jackson, P., Reichgelt, H., & van Harmelen, F. (1989). Logic-Based Knowledge Representation. The MIT Press, Cambridge, MA.Google Scholar
- Lavrač, N. & Vassilev, H. (1989). Meta-level architecture of a second-generation knowledge acquisition system. In Morik, K., editor, Proceedings EWSL-89, pages 99–109, London. Pitman.Google Scholar
- McDermott, J. (1988). 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
- Steels, L. (1990). Components of expertise. AI Magazine. Also as: AI Memo 88-16, AI Lab, Free University of Brussels.Google Scholar
- van Harmelen, F., Akkermans, H., Balder, J., Schreiber, G., & Wielinga, B. (1990). Formal specifications of knowledge models. ESPRIT Basic Research Action P3178 REFLECT, Technical Report RFL/ECN/I.4/1, Netherlands Energy Research Foundation ECN.Google Scholar
- Weyhrauch, R. (1980). Prolegomena to a theory of mechanized formal reasoning. Artificial Intelligence, 13. 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
- Wielinga, B. & Breuker, J. (1986). Models of expertise. In Proceedings ECAI-86, pages 306–318.Google Scholar
- Wielinga, B. J., Schreiber, A. T., & Breuker, J. A. (1992). KADS: A modelling approach to knowledge engineering. Knowledge Acquisition, 4(1). Special issue “The KADS approach to knowledge engineering”.Google Scholar