Abstract
This paper describesfoil, a system that learns Horn clauses from data expressed as relations.foil is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.
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Anderson, J.R., and Kline, P.J. (1979). A learning system and its psychological implications.Proceedings of the Sixth International Joint Conference on Artificial Intelligence, (pp. 16–21). Tokyo, Japan.
Bratko I. (1986).Prolog programming for artificial intelligence. Wokingham: Addison-Wesley.
Brebner P.C. (1988).Gargantubrain: An heuristic algorithm for learning non-recursive Horn clauses from positive and negative examples. (Technical Report 8802). Sydney, Australia: University of New South Wales, School of Electrical Engineering and Computer Science.
Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. (1984).Classification and regression trees. Belmont: Wadsworth.
Cestnik B., Kononenko I., and Bratko I. (1987).assistant 86: A knowledge elicitation tool for sophisticated users. In I. Bratko and N. Lavrač (Eds.),Progress in machine learning. Wilmslow: Sigma Press.
Clark P., and Niblett T. (1987). Induction in noisy domains. In I. Bratko and N. Lavrač (Eds.),Progress in machine learning. Wilmslow: Sigma Press.
Clark P., and Niblett T. (1989). Thecn2 induction algorithm.Machine Learning,3, 261–284.
DeJong G., and Mooney R. (1986). Explanation-based learning: An alternative view.Machine Learning,1, 145–176.
Dietterich T.G., and Michalski R.S. (1981). Inductive learning of structural descriptions.Artificial Intelligence,16, 257–294.
Dietterich T.G., and Michalski R.S. (1986). Learning to predict sequences. In R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Eds.),Machine learning: An artificial intelligence approach (Vol 2). Los Altos: Morgan Kaufmann.
Hayes-Roth F., and McDermott J. (1977). Knowledge acquisition from structural descriptions.Proceedings of the Fifth International Joint Conference on Artificial Intelligence (pp. 356–362). Cambridge, MA: Morgan Kaufmann.
Hinton G.E. (1986). Learning distributed representations of concepts.Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA: Lawrence Erlbaum.
Hunt E.B., Marin J., and Stone P.J. (1966).Experiments in induction. New York: Academic Press.
Langley P. (1985). Learning to search: From weak methods to domain-specific heuristics.Cognitive Science,9, 217–260.
Manago M. (1989). Knowledge intensive induction. InProceedings of the Sixth International Machine Learning Workshop. Ithaca, NY: Morgan Kaufmann.
Michalski R.S. (1980). Pattern recognition as rule-guided inductive inference.IEEE Transactions on Pattern Analysis and Machine Intelligence,2, 349–361.
Michalski R.S., Mozetič I. Hong J., and Lavrač N. (1986). The multipurpose incremental learning systemaq15 and its testing application to three medical domains.Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 1041–1045). Philadelphia, PA: Morgan Kaufmann.
Mitchell T.M., Keller R.M., and Kedar-Cabelli S.T. (1986). Explanation-based generalization: A unifying view.Machine Learning,1, 47–80.
Muggleton S., and Buntine W. (1988). Machine invention of first-order predicates by inverting resolution.Proceedings of the Fifth International Conference on Machine Learning (pp. 339–352). Ann Arbor, MI: Morgan Kaufmann.
Muggleton S., Bain M., Hayes-Michie J., and Michie D. (1989). An experimental comparison of human and machine learning formalisms.Proceedings of the Sixth International Machine Learning Workshop (pp. 113–188). Ithaca, NY: Morgan Kaufmann.
Quinlan J.R. (1979). Discovering rules by induction from large collections of examples. In D. Michie (Ed.),Expert systems in the micro electronic age. Edinburgh: Edinburgh University Press.
Quinlan J.R. (1986). Induction of decision trees.Machine Learning,1, 81–106.
Quinlan J.R. (1987). Simplifying decision trees.International Journal of Man-Machine Studies,27, 221–234.
Quinlan J.R. (1988). Decision trees and multi-valued attributes. In J.E. Hayes, D. Michie and J. Richards (Eds.),Machine Intelligence 11. Oxford: Oxford University Press.
Quinlan J.R., and Rivest R.L. (1989). Inferring decision trees using the Minimum Description Length principle.Information and Computation,80, 227–248.
Rissanen J. (1983). A universal prior for integers and estimation by minimum description length.Annals of Statistics,11, 416–431.
Rivest R.L. (1988). Learning decision lists.Machine Learning,2, 229–246.
Sammut C.A., and Banerji R.B. (1986). Learning concepts by asking questions. In R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Eds.),Machine Learning: An artificial intelligence approach (Vol 2). Los Altos: Morgan Kaufmann.
Schlimmer J.C., and Fisher D. (1986). A case study of incremental concept formation.Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 296–501). Philadelphia, PA: Morgan Kaufmann.
Shapiro E.Y. (1981). An algorithm that infers theories from facts.Proceedings of the Seventh International Joint Conference on Artificial Intelligence (pp. 446–451). Vancouver, BC: Morgan Kaufmann
Shapiro E.Y. (1983).Algorithmic program debugging. Cambridge, MA: MIT Press.
Vere S.A. (1978). Inductive learning of relational productions. In D.A. Waterman and F. Hayes-Roth (Eds.),Pattern-directed inference systems, New York: Academic Press.
Winston P.H. (1975). Learning structural descriptions from examples. In P.H. Winston (Ed.),The psychology of computer vision. New York: McGraw-Hill.
Winston P.H. (1984).Artificial intelligence (2nd Ed.). Reading: Addison-Wesley.
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Quinlan, J.R. Learning logical definitions from relations. Mach Learn 5, 239–266 (1990). https://doi.org/10.1007/BF00117105
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DOI: https://doi.org/10.1007/BF00117105