Abstract
Inductive Logic Programming deals with the problem of generating logic programs from examples, normally given as ground atoms. We briefly survey older methods (Shapiro’s MIS and Plotkin’s least general generalizations) which have set the foundations of the field and inspired more recent top-down and bottom-up approaches, respectively. Recent research has suggested that practical program induction requires a hypothesis space which is restricted a priori. We show that, if this trend is brought to the extreme consequence of requiring a well-defined and finite set of allowed clauses, efficient induction procedures can be devised to produce programs which are consistent and complete with the examples. On this basis, we suggest that “Examples + Hypothesis Space” can become an alternative way to specify a logic program. Software can be developed and reused by adding or modifying examples, and by refining the set of allowed clauses. Inductive synthesis is then proposed as a software engineering tool for the development, reuse and testing of logic programs.
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References
F. Bergadano. The Problem of Induction and Machine Learning. In Proc. Int. Joint Conf. on Artificial Intelligence, pages 1073–1079, Sydney, Australia, 1991.
F. Bergadano. Inductive Database Relations. IEEE Trans. on Data and Knowledge Engineering, 5(6), 1993.
F. Bergadano, A. Giordana, and L. Saitta. Automated Concept Acquisition in Noisy Environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4):555–578, 1988. New York.
F. Bergadano and D. Gunetti. An interactive system to learn functional logic programs. In Proc. 13th Int. Joint. Conf. on Artificial Intelligence, pages 1044–1049, Chambery, France, 1993. Morgan Kaufmann.
R. A. DeMillo and A. J. Offutt. Constraint-Based Automatic Test Data Generation. IEEE Trans. on Software Engineering, 17 (9): 900–910, 1991.
M. E. Gold. Language Identification in the Limit. Information and Control, 10: 447–474, 1967.
W. E. Howden. Functional Program Testing. IEEE Trans. on Software Engineering, 6 (2): 162–169, 1980.
S. Muggleton. Machine Invention of First Order Predicates by Inverting Resolution. In Proc. of the Fifth Int. Conf. on Machine Learning, pages 339–352, Ann Arbor, MI, 1988.
S. Muggleton. Inductive Logic Programming. New Generation Computing, 8 (4): 295–318, 1991.
M. Pazzani and D. Kibler. The Utility of Knowledge in Inductive Learning. Machine Learning, 9: 57–94, 1992.
G. Plotkin. A note on Inductive Generalization. In B. Meltzer and D. Michie, editors, Machine Intelligence 5, pages 153–163, 1970.
R. Quinlan. Learning Logical Definitions from Relations. Machine Learning, 5: 239–266, 1990.
E. Y. Shapiro. Algorithmic Program Debugging. MIT Press, 1983.
E. J. Weyuker. Assessing Test Data Adequacy through Program Inference. ACM Trans. on Programming Languages and Systems, 5 (4): 641–655, 1983.
R. Wirth. Completing logic programs by inverse resolution. In Proc. European Working Sessions on Learning, pages 239–250, Montpellier, France, 1989. Pitman.
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© 1994 British Computer Society
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Bergadano, F., Gunetti, D. (1994). Inductive Synthesis of Logic Programs and Inductive Logic Programming. In: Deville, Y. (eds) Logic Program Synthesis and Transformation. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3234-9_4
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DOI: https://doi.org/10.1007/978-1-4471-3234-9_4
Publisher Name: Springer, London
Print ISBN: 978-3-540-19864-2
Online ISBN: 978-1-4471-3234-9
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