Abstract
In the paper, we present some learning tasks that cannot be solved by two wellknown systems, FOIL and FOCL. Two kinds of explanations can be provided for these failures. For some tasks, the failures can be ascribed to a wrong definition of the space in which these systems perform the search for logical definitions. By moving from θ-subsumption to a weaker, but more mechanizable and manageable, model of generalization, called θOI-subsumption, a new search space is defined in which such tasks can be solved. Such a solution has been implemented in a new version of FOCL, called FOCL-OI. However, other learning tasks cannot be solved by changing the search space. For these tasks, the conceptual problem detected both in FOIL and in FOCL concerns the generation of meaningless rules, which do not mirror at all the structure of the training instances. We claim that, whenever possible, the training/test examples should be represented as ground Horn clauses, rather than as tuples of a relational database or facts of a Prolog database.
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Bell, S., and Weber, S., On the close logical relationship between FOIL and the frameworks of Helft and Plotkin, Proceedings of The Third International Workshop on Inductive Logic Programming ILP'93, Bled, Slovenia, 1–10, 1993.
Bergadano, F., Giordana, A., and Saitta, L., Automated concept acquisition in noisy environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-10, 555–578, 1988.
Datta, P., and Kibler, D., Concept Sharing: A Means to Improve Multi-Concept Learning, Proceedings of the 10th International Conference on Machine Learning, Amherst, MA, 89–96, 1993.
Esposito, F., Automated acquisition of production rules by empirical supervised learning methods, in Data, Expert Knowledge and Decisions, (Vol. II), M. Schader (Ed.), Springer-Verlag, Heidelberg, Germany, 1990.
Esposito, F., Malerba, D., Semeraro, G., and Pazzani, M., A Machine Learning Approach To Document Understanding, Proceedings of the 2nd International Workshop on Multistrategy Learning MSL-93, Harpers Ferry, West Virginia, 276–292, 1993.
Esposito, F., Malerba, D., and Semeraro, G., Multistrategy Learning for Document Recognition, Applied Artificial Intelligence 8, 33–88, 1994.
Esposito F., Malerba, D., Semeraro, G., Brunk, C., and Pazzani, M., Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL, in Pre-proceedings of LOPSTR 94 — Fourth Workshop on Logic Program Synthesis and Transformation, Pisa, Italy, June 20–21, 1994.
Garey, M.R., and Johnson, D.S., Computers and Intractability, Freeman, San Francisco, CA, 1979.
Genesereth, M.R., and Nilsson, N.J., Logical Foundations of Artificial Intelligence, Morgan Kaufmann, Palo Alto, CA, 1987.
Hayes-Roth, F., Schematic classification problems and their solution, Pattern Recognition, 105–113, 1974.
Helft, N., Inductive Generalization: A Logical Framework, in Progress in Machine Learning-Proceedings of EWSL 87, I. Bratko & N. Lavrac (Eds.), Sigma Press, Bled, Yugoslavia, 149–157, 1987.
Knight, K., Unification: A Multidisciplinary Survey, ACM Computing Surveys, Vol.21, No.1, 1989.
Lloyd, J.W., Foundations of Logic Programming, Second Edition, Springer-Verlag, New York, 1987.
Malerba, D., Document Understanding: A Machine Learning Approach, Technical Report, Esprit Project 5203 INTREPID, March 1993.
Michalski, R.S., Pattern Recognition as Rule-Guided Inductive Inference, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, 349–361, 1980.
Muggleton, S., Inductive Logic Programming, New Generation Computing, 8(4), 295–318, 1991.
Pazzani, M.J., and Brunk, C., FOCL-1-2-3 Version 1.1, Department of Information and Computer Science, University of California, Irvine, California, March 1993.
Pazzani, M., and Kibler, D., The utility of knowledge in inductive learning, Machine Learning 9, 1, 57–94, 1992.
Plotkin, G.D., A Note on Inductive Generalization, in Machine Intelligence 5, B. Meltzer and D. Michie (Eds.), 153–163, Edinburgh University Press, 1970.
Quinlan, J. R., Learning Logical Definitions from Relations, Machine Learning 5, 3, 239–266, 1990.
Quinlan, J.R., Determinate Literals in Inductive Logic Programming, Proceedings of the 11th International Joint Conference on Artificial Intelligence, Sydney, Australia, 746–750, 1991.
Quinlan, J.R., Cameron-Jones, R.M., FOIL:amidterm report, in Machine Learning:ECML-93-Proceedings of the Sixth European Conference on Machine Learning, Lecture Notes in Artificial Intelligence 667, Pavel B. Brazdil (Ed.), Springer-Verlag, Vienna, Austria, 3–20, 1993.
Rissanen, J., A universal prior for integers and estimation by minimum description length, Annals of Statistics, 11, 1, 416–431, 1983.
Semeraro, G., Brunk, C.A., and Pazzani M.J., Traps and Pitfalls when Learning Logical Theories: A Case Study with FOIL and FOCL, Technical Report 93-33, Department of Information and Computer Science, University of California, Irvine, California, July 26, 1993.
Semeraro, G., Esposito, F., and Malerba, D., Learning Contextual Rules for Document Understanding, Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications, San Antonio, Texas, 108–115, 1994.
Silverstein, G., and Pazzani, M., Relational clichés: constraining constructive induction during relational learning, Proceedings of the Eighth International Workshop on Machine Learning, Evanston, Illinois, 203–207, 1991.
Stepp, R.E., Conjunctive Conceptual Clustering: A Methodology and Experimentation, Ph.D. dissertation, University of Illinois at Urbana-Champaign, Urbana, Illinois, UIUCDCS-R-84-1189, 1984.
Vere, S.A., Induction of relational productions in the presence of background information, Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, 349–355, 1977.
Winston, P.H., Learning Structural Descriptions from Examples, Ph.D. dissertation, Dept. of Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, January 1970.
Wirth, R., and O'Rorke, P., Constraints on Predicate Invention, Proceedings of the Eighth International Workshop on Machine Learning, Evanston, Illinois, 457–461, 1991.
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Esposito, F., Malerba, D., Semeraro, G., Brunk, C., Pazzani, M. (1994). Traps and pitfalls when learning logical definitions from relations. In: Raś, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1994. Lecture Notes in Computer Science, vol 869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58495-1_38
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DOI: https://doi.org/10.1007/3-540-58495-1_38
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