Abstract
In this paper we will report our current research on the NOOS language, an attempt to provide a uniform representation framework for inference and learning components supporting flexible and multiple combination of these components. Rather than a specific combination of learning methods, we are interested in an architecture adaptable to different domains where multiple learning strategies (combinations of learning methods) can be programmed. Our approach derives from the knowledge modelling frameworks developed for the design and construction of KBSs based on the task/method decomposition principle and the analysis of knowledge requirements for methods. Our thesis is that learning methods are methods with introspection capabilities that can be also analyzed in the same task/method decomposition. In order to infer new decisions from the results and behavior of other inference processes, those results and behavior have to be represented and stored in the memory for the learning method to be able to work with them.
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References
Akkermans, H., van Harmelen, F., Schreiber, G., Wielinga, B.: A formalisation of knowledge-level model for knowledge acquisition. Int Journal of Intelligent Systems, 8 (1993) 169–208.
Armengol, E., Plaza, E.: Integrating induction in a case-based reasoner. Proc. 2nd European Workshop on Case-based Reasoning, (to appear).
Carbonell, J. G.: Derivational analogy and its role in problem solving. Proc. AAAI-83 (1983) 45–48.
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).
Greiner, R., Lenat, D.: RLL-1: A Representation Language Language, HPP-80-9 Comp. Science Dept., Stanford University (1980). Expanded version of the same paper in Proc. First AAAI Conference.
Kiczales G., Des Rivières J., Bobrow D. G.: The Art of the Metaobject Protocol, The MIT Press: Cambridge (1991).
Mitchell, T.M., Allen, J., Chalasani, P., Cheng, J., Etzioni, O. Ringuette, M., Schlimmer, J. C.: Theo: a fra-mework for self-improving systems. In K Van Lenhn (Ed.) Architectures for Intelligence. Laurence Erlbaum, (1991).
Newell, A.: Unified Theories of Cognition. Cambridge MA: Harvard University Press (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, November (1992) 166–171.
Plaza, E., Arcos J. L.: Reflection and Analogy in Memory-based Learning, Proc. Multistrategy Learning Workshop (1993) 42–49.
Puerta, A., Egar, J., Tu, S., Musen, M. A.: A multiple-method knowledge acquisition shell for the automatic generation of knowledge acquisition tools. In Procs. of the AAAI Knowledge Acquisition Workshop (1991).
Russell, S.: The Use of Knowledge in Analogy and Induction. Morgan Kaufmann (1990).
Slodzian, A.: Configuring decision tree learning algorithms with KresT, Knowledge level models of machine learning Workshop preprints. Catania, Italy (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, (1985) 31–40.
Steels, L.: The Components of Expertise, AI Magazine, 11 (1990) 30–49.
van Harmelen, F., Balder, J. R.: (ML)2: A formal language for KADS models of expertise. Knowledge Acquisition, 4 (1992).
van Marcke, K.: KRS: An object-oriented representation language, Revue d'Intelligence Artificielle, 1 (1987) 43–68.
Van de Velde, W.: Towards Knowledge Level Models of Learning Systems, Knowledge level models of machine learning Workshop preprints. Catania, Italy, April (1994).
Veloso, M.: Learning by analogical reasoning in general problem solving. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA (1992).
Wielinga, B., Schreiber, A., Breuker, J.: KADS: A modelling approach to knowledge engineering. Knowledge Acquisition 4 (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, Springer Verlag: Berlin, (1993) 299–335.
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Arcos, J.L., Plaza, E. (1994). Integration of learning into a knowledge modelling framework. In: Steels, L., Schreiber, G., Van de Velde, W. (eds) A Future for Knowledge Acquisition. EKAW 1994. Lecture Notes in Computer Science, vol 867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58487-0_19
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DOI: https://doi.org/10.1007/3-540-58487-0_19
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