Abstract
In this paper we will discuss the role of case-based reasoning and learning as a tool for integrating different methods of inference and different methods of learning. The Massive Memory Architecture, an experimental framework for experience-based learning and reasoning, is described. Its reflective capabilities are described and we put forth the hypothesis that learning methods are inference methods able to inspect the problem solving process and modify the system itself so as to improve its behavior. Therefore, learning methods require a self-model of the system. Self-models and method implementation are based on conceptual, knowledge-level descriptions of inference.
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References
Aamodt, A., Knowledge-intensivecase-based reasoning and learning. Proc. ECAI-90, Stockholm, August 1990.
Akkermans, H., van Harmelen, F., Schreiber, G., Wielinga, B., A formalisation of knowledge-level model for knowledge acquisition. Int Journal of Intelligent Systems forthcoming, 1993.
Armengol, E. and Plaza E., Analyzing case-based reasoning at the knowledge level. European Workshop on Case-based Reasoning EWCBR'93, 1994.
Carbonell, J., Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning, Vol. II. Morgan Kaufmann, pp. 371–392, 1986.
Carbonell, J. G., Knoblock, C. A., Minton, S., Prodigy: An integrated architecture for planning and learning. In K Van Kehn (Eds.), Architectures for Intelligence. Lawrence Erlbaum Ass., Hillsdadale, NJ, 1991.
Chandrasekaran, B., Task structures, knowledge acquisition and machine learning. Machine Learning 2:341–347, 1989.
Giunchilia, F., and Traverso, P., Plan formation and execution in an architecture of declarative metatheories. Proc of META-90: 2nd Workshop of Metaprogramming in Logic Programming. MIT Press, 1990.
Godo, L., López de Màntaras, R., Sierra, C., Verdaguer, A., MILORD: The architecture and the management of linguistically expressed uncertainty. Int. J. Intelligent Systems, 4:471–501, 1989.
Greiner, R., Lenat, D. RLL-1: A Representation Language Language, HPP-80-9 Comp. Science dept., Stanford University. Expanded version of the same paper in Proc. First AAAI Conference., 1980.
Kiczales G., Des Rivières J., Bobrow D. G., The Art of the Metaobject Protocol, The MIT Press: Cambridge, 1991.
López, B. and Plaza, E., Case-based planning for medical diagnosis, In Z Ras (Ed.) Methodologies for Intelligent Systems. Lecture Notes in Artificial Intelligence, 689, p. 96–105. Springer-Verlag, 1993.
Mitchell, T.M., Allen, J., Chalasani, P., Cheng, J., Etzioni, O., Ringuette, M., Schlimmer, J. C., Theo: a framework for self-improving systems. In K Van Lenhn (Ed.) Architectures for Intelligence. Laurence Erlbaum, 1991.
Newell, A., Unified Theories of Cognition. Cambridge MA: Harvard UniversityPress, 1990.
Plaza, E, Reflection for analogy: Inference-level reflection in an architecture for analogical reasoning. Proc. IMSA'92 Workshop on Reflection and Metalevel Architectures, Tokyo, p. 166–171, November 1992.
Plaza, E. Arcos J. L., Reflection and Analogy in Memory-based Learning, Proc. Multistrategy Learning Workshop, p. 42–49, 1993.
Plaza, E. Arcos J. L., Flexible Integration of Multiple Learning Methods into a Problem Solving Architecture, Reserch Report 93/16 1993.
Ram, A., Cox, M. T., Narayanan, S., An architecture for integrated introspective learning. Proc. ML'92 Workshop on Computational Architectures for Machine Learning and Knowledge Acquisition, 1992.
Russell, S., The use of knowledge in analogy and induction. Morgan Kaufmann, 1990.
Sierra, C., and Godo, L. Specifying simple scheduling tasks in a reflective and modular architecture. In J Treur and T Wetter (Eds.) Formal Specifications Methods for Complex Reasoning Systems,.Ellis Horwood, pp. 199–232, 1993.
Slodzian, A., Configuring decision tree learning algorithms with KresT, Knowledge level models of machine learning Workshop preprints. Catania, Italy, April 1994.
Smith, B. C., Reflection and semantics in a procedural language, In Brachman, R. J., and Levesque, H. J. (Eds.) Readings in Knowledge Representation. Morgan Kauffman, California, pp. 31–40, 1985.
Steels, L., The Components of Expertise, AI Magazine, Summer 1990.
Treur, J., On the use of reflection principles in modelling complex reasoning. Int. J. Intelligent Systems, 6:277–294, 1991.
Van de Velde, W., Towards Knowledge Level Models of Learning Systems, Knowledge level models of machine learning Workshop preprints. Catania, Italy, April 1994.
van Marcke, K., KRS: An object-oriented representation language, Revue d'Intelligence Artificielle, 1(4), 43–68, 1987.
Wielinga, B., Schreiber, A., Breuker, J., KADS: A modelling approach to knowledge engineering. Knowledge Acquisition 4(1), 1992.
Wielinga, B., Van de Velde, W., Schreiber, G., Akkermans, H., Towards a Unification of Knowledge Modelling Approaches. In J.-M. David, J.-P. Krivine and R. Simmons (eds.). Second generation Expert Systems. pp 299–335. Springer Verlag, 1993.
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Arcos, J.L., Plaza, E. (1994). A reflective architecture for integrated memory-based learning and reasoning. In: Wess, S., Althoff, KD., Richter, M.M. (eds) Topics in Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science, vol 837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58330-0_94
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DOI: https://doi.org/10.1007/3-540-58330-0_94
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