Controlling inductive search in rigel learning system
Concept induction from examples has already proved to work on significant case studies and will be a fundamental tool in next generation expert systems. For this reason it is important to improve induction techniques, particularly to face real world problems. A tipical problem of real application is the width of the state spaces to be searched. This problem has been faced in the design of the inductive learning system RIGEL, which explicitly represents and uses task dependent (but domain independent) control knowledge to strongly focus the inductive search. RIGEL has been implemented in Common Lisp on TI Explorer, Symbolics and Sun and tested on several case studies taken from literature. At present it is beeing applied to a real problem, taken from the image recognition field.
Unable to display preview. Download preview PDF.
- J. Carbonell, R. Michalsky, T. Mitchell: "Machine Learning", Vol I–II Tioga Publishing Company, (Palo Alto, 1983–1986).Google Scholar
- T. Ditterich, R.S. Michalsky: "A Comparative Review of Selected Methods For Learning from Examples", in Machine Learning, Vol. I, R. S. Michalski, J. Carbonell and T. Mitchell (Eds.), Tioga Publishing Company, (Palo Alto 1983), pp. 41–81.Google Scholar
- R.S. Michalski: "A Theory and Methodology of Inductive Learning", in Machine Learning, Vol. I, R. S. Michalski, J. Carbonell and T. Mitchell (Eds.), Tioga Publishing Company, (Palo Alto 1983), pp. 83–134.Google Scholar
- R.S. Michalsky: "Pattern Recognition as Rule-Guided Inductive Inference", in IEEE Transactions on pattern analysys and Machine Intelligence, Vol. PAMI-2, (1980), pp. 349–361.Google Scholar
- R.S. Michalski, E.R. Stepp: "INDUCE 3: A program for Learning Structural Descriptions from Examples", Internal Report, Departement of Computer Science, University of Illinois at Urbana-Champaign.Google Scholar
- T.M. Mitchell: "Generalization as search", Artificial Intelligence, 18, (1982), pp. 203–226.Google Scholar
- S. Weber: "General Concept of Fuzzy Connectives, Negations and Implication, based on t-norm and t-conorm", i n Fuzzy Set and System, Vol 11, (1983), pp. 115–134.Google Scholar