Abstract
Cohen's [1] refinement rules provide a flexible mechanism for introducing intentional background knowledge in an ILP system. Whereas Cohen used a limited second order theorem prover to imple- ment the rule interpreter, we extend the method to use a full Prolog interpreter. This makes the introduction of more complex background knowledge possible. Although refinement rules have been used to gener- ate literals for a general-to-specific search, we show how they can also be used as filters to reduce the number of literals in an RLGG algorithm. Each literal constructed by the LGG is tested against the refinement rules and only admitted if a refinement rule has been satisfied.
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References
Cohen, W.: Learning to classify English text with ILP methods. In L. de Raedt (Eds.), Advances in Inductive Logic programming. IOS Press (1996) 124–143
Muggleton, S., Feng, C.: Efficient induction of logic programs. In First Conference on Algorithmic Learning Theory. Omsha, Tokyo (1990)
Muggleton, S.: Inverse Entailment and Progol. New Generation Computing 13 (1995) 245–286.
Page, C. D., Frisch, A. M.: Generalization and Learnability: A study of constrained atoms. In S. Muggleton (Eds.): Inductive Logic Programming. Academic Press (1992) 29–61
Plotkin, G. D.: A further note on inductive generalization. In B. Meltzer and D. Michie (Eds.): Machine Intelligence 6. Elsevier, New York (1971)
Quinlan, J. R.: Learning Logical Definitions from Relations. Machine Learning 5 (1990) 239–266
Rouveirol, C., Puget, J.-F.: Beyond Inversion of Resolution. In Proceedings of the Seventh International Conference on Machine Learning. Morgan Kaufmann (1990)
Sammut, C. A., Banerji, R. B.: Learning Concepts by Asking Questions. In R. S. Michalski Carbonell, J.G. and Mitchell, T.M. (Eds.): Machine Learning: An Artificial Intelligence Approach, Vol 2. Morgan Kaufmann, Los Altos, California (1986) 167–192
Sammut, C.: Using background knowledge to build multistrategy learners. Machine Learning 27 (1997) 241–257
Srinivasan, A., Camacho, R.: Experiments in numerical reasoning with Inductive Logic Programming. Journal of Logic Programming (in press)
Zrimec, T., Sammut, C.A.: A Medical Image Understanding System. Engineering applications of Artificial Intelligence 10(1) (1997) 31–39.
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© 1998 Springer-Verlag Berlin Heidelberg
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Sammut, C. (1998). Prolog, refinements and RLGG's. In: Page, D. (eds) Inductive Logic Programming. ILP 1998. Lecture Notes in Computer Science, vol 1446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027326
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DOI: https://doi.org/10.1007/BFb0027326
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