Abstract
The paper deals with the problem of improving problem-solving behavior of traditional KBS in the course of its real operation which is a part of the maintenance task. The solution of the problem is searched in integration of the KBS with a specially designed case-based reasoning module used for correcting solutions produced by the KBS. Special attention is paid to the methods of case matching and reconciling conflicts between CBR and RBR. The proposed solution for both problems is based on treating the maintenance task as a problem for learning two-tiered domain representation. From this view point rules form the first domain tier reflecting existing strong patterns in the representation of domain concepts, while the second tier is formed by the newly solved cases along with a special domain-dependent procedure for case matching. The main ideas of the approach are illustrated by the results of some experiments with the experimental system CoRCase.
This research was partially supported by National Science Foundation, Grant I-416/94
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References
Aamodt, A.: Knowledge-intensive, integrated approach to problem solving and sustained learning. Ph.D. Dissertation, University of Trondheim (1991).
Agre, G.: Improvement of KBS Behavior by Using Problem-Solving Experience. Ph. Jorrand and V. Sgurev (Eds.), Proc. of the VIth Int. Conference AIMSA '94, World Scientific Publ., Singapore (1994) 257–266.
Agre, G.: An Approach to Integration of Rule-Based and Case-Based Reasoning. Problems of Engineering Cybernetics and Robotics, 42, Bulgarian Academy of Sciences, Sofia (1995) 40–49.
Barsalou, L., Hale, C.: Components of conceptual representation: from feature lists to recursive frames. I. Van Mechelen, J. Hampton, R.S. Michalsi and P. Theuns (Eds.) Categories and Concepts — Theoretical Views and Inductive Data Analysis, Academic Press (1993) 97–144.
Bergadano, F., Matwin, S., Michalski R.S., Zhang, J.: Learning two-tiered description of flexible concepts: the POSEIDON system. Machine Learning 8 (1992) 5–43.
Biberman, Y.: A Contex Similarity Measure. F. Bergadano and L. De Raedt (Eds.) Machine Learning: ECML-94. LNAI 784, Springer-Verlag (1994) 48–63
Coenen, F., Bench-Capon, T.: Maintenance of Knowledge-Based Systems: Theory, Techniques and Tools, Academic Press (1993).
Golding A.R., Rosenbloom, P.S.: Improving rule-based systems through case-based reasoning. Proceedings of the National Conference on Artificial Intelligence, Anaheim, MIT Press (1991) 22–27.
Hammond, K.: Case-Based Planning: Viewing Planning as a Memory Task. Academic Press (1989).
Iordanova I., Giacometti, A., Vila, A., Amy, B., Reymond, F., Abaoub, L., Dahou M., Rialle, V.: Shade — A Hybrid System for Diagnosis in Electromyography. Proc. of IXth Int. Congress on Electromyography, Jerusalem (1992).
Kolodner, J.L.: Extending problem solver capabilities through case-based inference. Proc. of 4th Workshop on Machine Learning, UC-Irvine, June 22–25 (1987) 167–178.
Markov, Z.: Private communication (1995).
Matwin, S., Plante, B.: A Deductive-Inductive Method For Theory Revision. R.S. Michalski and Gh. Tecuci (Eds.) Proc. of the First Int. Workshop on Multistrategy Learning (1991) Harpers Ferry, 160–174.
Michalski R.S.: Learning flexible concepts: fundamental ideas and a method based on two-tiered representation. Y. Kodratoff and R.S. Michalski (Eds.) Machine Learning: an Artificial Intelligence Approach 3, San Mateo, CA: Morgan Kaufmann (1990) 63–111.
Mitchell, T.M., R. Keller, Kedar-Cabelli, S.: Explanation-Based Generalization: A unifying view. Machine Learning 1 (1986) 47–80.
Cost, S., Salzberg, S.: A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning 10(1) (1991) 56–78.
Stanfill, C., Waltz, D.: Toward memory-based reasoning. Communication of ACM 29(12) (1986) 1213–1229.
Zhang, J.: Integrating Symbolic and Subsymbolic Approaches in Learning Flexible Concepts. R.S. Michalski and Gh. Tecuci (Eds.) Proc. of the First Int. Workshop on Multistrategy Learning, Harpers Ferry (1991) 289–304.
Zhang, J.: Selecting Typical Instances in Instance-Based Learning. D. Sleeman and P. Edwards (Eds.) Machine Learning — Proc. of the Ninth Int. Workshop (ML02), San Mateo, CA: Morgan Kaufmann (1992) 470–479
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Agre, G. (1995). KBS maintenance as learning two-tiered domain representation. In: Veloso, M., Aamodt, A. (eds) Case-Based Reasoning Research and Development. ICCBR 1995. Lecture Notes in Computer Science, vol 1010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60598-3_11
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DOI: https://doi.org/10.1007/3-540-60598-3_11
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