Knowledge-based systems are becoming increasingly model oriented. Models enable the system a deeper understanding — something which is impractical to attain when all the system has are rules. Furthermore, it has become apparent that knowledge representations must become increasingly domain-specific in order to facilitate more sophisticated problem solving. The task of automating the solution of sophisticated problems in turn implies the use of analogic reasoning towards the goal of automatic knowledge acquisition.
The approach taken here is to investigate new machine learning algorithms focusing on lateral model-based transformative induction methods similar to Quinlan's ID3 and Michalski's AQ algorithms — except that models are the generalized object(s) rather than simply decision trees or rules.
KeywordsCase-based reasoning distributed computation machine learning transformation
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- M. Bruynooghe, L. De Raedt, and D. De Schreye. Explanation Based Program Transformation. IJCAI-89, 1989, pp. 407–412.Google Scholar
- J.G. Carbonell. Derivational Analogy and its Role in Problem Solving. AAAI-83, 1983, pp. 64–69.Google Scholar
- J.G. Carbonell. Introduction: Paradigms for Machine Learning. Artificial Intelligence, Special Volume on Machine Learning, 40(1–3), September 1989, pp. 1–9.Google Scholar
- T.R. Davies and S.J. Russel. A Logical Approach to Reasoning by Analogy. IJCAI-87, 1987.Google Scholar
- N. Dershowitz. The Evolution of Programs. Birkhauser, Boston, MA, 1983.Google Scholar
- B. Falkenhainer, K. Forbus, and D. Gentner. The Structure Mapping Engine. AAAI-86, August 1986.Google Scholar
- M.T. Harandi and S. Bhansali. Program Derivation Using Analogy. IJCAI-89, August 1989, pp. 389–394.Google Scholar
- M. Manago. Knowledge Intensive Induction. Proceedings of the Sixth International Workshop on Machine Learning, 1989, pp. 151–155.Google Scholar
- R.S. Michalski and R.L. Chilausky. Learning by being told and learning from examples. International Journal of Policy Analysis and Information Systems, 4(2), 1980, pp. 125–161.Google Scholar
- R.S. Michalski, J.G. Carbonell, and T.M. Mitchell. Machine Learning, Volume II. Morgan Kaufman Publishers, Los Altos, CA, 1986.Google Scholar
- S. Minton. Learning Search Control Knowledge: An Explanation-Based Approach, Kluwer Academic Publishers, Boston, MA, 1988.Google Scholar
- T.M. Mitchell. "Version Spaces: A Candidate Elimination Approach to Rule Learning", in Proceedings Fifth International Joint Conference on Artificial Intelligence, 1977, pp. 305–310.Google Scholar
- T.M. Mitchell, R. Keller, and S. Kedar-Cabelli. Explanation-Based Generalization: A Unifying View. Machine Learning, 1(1), 1986, pp. 47–80.Google Scholar
- Office of Naval Technology. Post Doctoral Fellowship Program, 1987–88, ASEE, Projects Office, 11 Dupont Circle, Suite 200, Washington, DC 20036.Google Scholar
- B.W. Porter. Similarity Assessment: Computation Vs. Representation. Proceedings of a Workshop on Case-Based Reasoning, Pensacola Beach, FL, June 1989, pp. 82–84.Google Scholar
- S.H. Rubin. Requirement-Driven Decision Support Systems. IEEE International Conference on Systems, Man, and Cybernetics, Cambridge, MA, November 1989.Google Scholar
- M.M. Veloso and J.G. Carbonell. Learning Analogies by Analogy — The Closed Loop of Memory Organization and Problem Solving. Proceedings of a Workshop on Case-Based Reasoning, Pensacola Beach, FL, June 1989, pp. 153–158.Google Scholar