Abstract
This paper presents an overview of explanation-based learning (EBL) where the descriptions of EBL methods are realized at the knowledge level. Knowledge level description is a proposal that emphasizes the knowledge content and usage and abstracts away implementation details. Analyzing six different EBL methods at the knowledge level it can be shown what is their shared structure and illuminates their differences in a common framework of description. To describe EBL we use the Components of Expertise (Steels 1991), a knowledge level methodology currently used to describe, analyze, and support the development of expert systems.
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References
Armengol, E. & Plaza, E. (1993). Elements of Explanation-based Learning. Research Repport IIIA 93/20.
Armengol, E. & Plaza, E. (1994). A Knowledge Level Model of Case-Based Reasoing.Lecture Notes in Artificial Intelligence. Springer Verlag 837: 53–64.
Clark, P. & Holte, R. (1992). Lazy Partial Evaluation: An Integration of EBG and Partial Evaluation.ML-92 Proc. of the Conf. on Machine Learning.
DeJong, G. & Mooney, R. (1986). Explanation-Based Learning: An Alternative View.Machine Learning 1.
Diettrich, T. G. (1986). Learning at the Knowledge Level.Machine Learning 1(3): 287–354.
Flann, N. S. & Dietterich, T. G. (1989). A Study of Explanation-Based Methods for Inductive Learning.Machine Learning 4(2): 187–226.
Laird, J. E., Newell, P. S. & Rosenbloom, P. S. (1986). SOAR:An Architecture for General Intelligence. Department of Computer Science. Carnegie-Mellon University.
Michalski, R. S. (1991). Inferential Learning Theory as a Basis for Multistrategy Task-Adaptive Learning. In Michalski, R. & Tecuci, G. (eds.)Proc. Int. Work.shop on Multistrategy Learning, 3–18. George Mason University.
Minton, S. (1988). Learning Effective Search Control Knowledge: An Explanation-Based Approach. Kluwer: Boston, MA.
Mitchell, T. M., Keller, R. M. & Kedar-Cabelli, S. T. (1986). Explanation-Based Generalization: A Unifiying View.Machine Learning 1.
Newell, A. (1982). The Knowledge Level.Artificial Intelligence 18: 87–127.
Nillson, N. J. (1980). Principles of Artificial Intelligence. Tioga: Palo Alto, CA.
Plaza, E. & Arcos, J. L. (1993). A Reflective Architecture for Integrated Memory-Based Learning and Reasoning. Proceedings of TheFirst European Workshop Case-Based Reasoning 1993, Lecture Notes in Artificial Intelligence. Springer Verlag (in press).
Plaza, E., Aamodt, A., Ram, A., van de Velde, W. & van Someren, M. (1993). Integrated Learning Architectures. In Brazdil, P. V. (ed.)Machine Learning: ECML-93, 429–441.Lecture Notes in Articicial Intelligence 667, Springer-Verlag.
Slodzian, A. (1994). Configuring Decison Tree Learning Algorithms with KREST.MLnet Familiarization Workshop on Knowledge Level Models for Machine Learning. Università di Catania.
Steels, L (1991). Components of Expertise.AI-Magazine.
Van de Velde, W. (1994). Towards Knowledge Level Models of Learning Systems.MLnet Familiarization Workshop on Knowledge Level Models for Machine Learning. Università di Catania.
Van Harmelen, F. & Bundy, A. (1988). Explanation-based Generalization = Partial Evaluation.Artificial Intelligence 36.
Waldinger, R. (1977). Achieving Several Goals Simultaneously.Machine Intelligence 8. E. Elcock & D. Michie (eds), Ellis Horwood: London.
Wielinga, B. Schreiber, A. & Breuker, J. (1992). KADS: A Modelling Approach to Knowledge Engineering.Knowledge Acquisition 4(1).
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Armengol, E., Plaza, E. Explanation-based learning: A knowledge level analysis. Artif Intell Rev 9, 19–35 (1995). https://doi.org/10.1007/BF00857652
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DOI: https://doi.org/10.1007/BF00857652