Inductive Logic Programming: From Logic of Discovery to Machine Learning

IEICE Transactions on Information and Systems

Volumn E83-D, Issue 1, 2000, Pages 10-18

  Arimura, Hiroki ^a,b   Yamamoto, Akihiro ^c  

^a KYUSHU UNIVERSITY   (Japan)

^b JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^c HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Inductive logic programming; Logic of discovery; Machine learning

Indexed keywords

INDUCTIVE LOGIC PROGRAMMING (ILP); LOGIC OF DISCOVERY;

COMPUTATIONAL LINGUISTICS; LEARNING ALGORITHMS; LEARNING SYSTEMS; THEOREM PROVING;

LOGIC PROGRAMMING;

EID: 0033895162     PISSN: 09168532     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. D. Angluin, "Learning with hints," Proc. COLT'88, pp.223-237, 1988.
2. D. Angluin, "Queries and concept learning," Machine Learning, vol.2, pp.319-342, 1988.
3. H. Arimura, H. Ishizaka, T. Shinohara, and S. Otsuki, "A generalization of the least general generalization," Machine Intelligence, vol.13, pp.59-85, Oxford University Press, 1994.
4. H. Arimura, "Learning acyclic first-order Horn sentences from entailment," Proc. 7th Int. Workshop on Algorithmic Learning Theory (LNAI 1316), pp.432-445, 1997.
5. H. Arimura, H. Ishizaka, and T. Shinohara, "Learning unions of tree patterns using queries," Theoretical Computer Science, vol.185, pp.47-62, 1997.
6. W. Buntine, "Generalized subsumption and its applications to induction and redundancy," Artificial Intelligence, vol.36, pp.149-176, 1988.
7. M. Frazier and L. Pitt, "Learning from entailment: An application to propositional Horn sentences," Proc. 10th Int. Conf. Machine Learning, pp.120-127, 1993.
8. M. Frazier and L. Pitt, "Classic learning," Proc. 7th COLT, pp.23-34, 1994.
9. K. Inoue, "Linear resolution for consequence finding," Artificial Intelligence, vol.56, pp.301-353, 1992.
10. B. Jung, "On inverting generality relations," Proc. 3rd International Workshop on Inductive Logic Programming, pp.87-101, 1993.
11. R. Khardon, "Learning function-free Horn expressions," Proc. COLT'98, pp.154-165, 1998.
12. J.W. Lloyd, Foundations of Logic Programming: Second, Extended Edition, Springer-Verlag, 1987.
13. S.U. Muggleton, "Inductive logic programming," Proc. 4th International Workshop on Algorithmic Learning Theory, pp.42-62, 1990.
14. S.-H. Nienhuys-Cheng and R. de Wolf, Foundations of Inductive Logic Programming (LNAI 1228), Springer, 1997.
15. C.D. Page, Anti-unification in constraint logics: Foundations and applications to learnability in first-order logic, to speed-up learning, and to deduction, PhD thesis, University of Illinois, 1993.
16. C.D. Page and A.M. Frisch, "Generalization and learnability: A study of constrained atoms," Inductive Logic Programming, pp.129-161, Academic Press, 1992.
17. G.D. Plotkin, "A note on inductive generalization," Machine Intelligence, vol.5, pp. 153-163, Edinburgh University Press, 1970.
18. G.D. Plotkin, "A further note on inductive generalization," Machine Intelligence, vol.6, pp.101-124, Edinburgh University Press, 1971.
19. G.D. Plotkin, Automatic methods of inductive inference, PhD thesis, Edinburgh University, 1971.
20. D. Poole, "A logical framework for default reasoning," Artificial Intelligence, vol.36, pp.27-47, 1988.
21. C. Roddy and P. Tadepalli, "Learning Horn definitions with equivalence and membership queries," Proc. 7th International Workshop on Inductive Logic Programming (LNAI 1297), pp.243-255, 1997.
22. C. Reddy and P. Tadepalli, "Learning first-order acyclic Horn programs from entailment," Proc. 15th International Conference on Machine Learning, 1998.
23. T. Sato and S. Akiba. "Inductive resolution," Proc. 4th International Workshop on Algorithmic Learning Theory (LNAI 744), pp.101-110, Springer-Verlag, 1993.
24. L.G. Valiant, "A theory of learnable," Commun. ACM, vol.27, pp.1134-1142, 1984.
25. A. Yamarnoto, "Representing inductive inference with SOLD-resolution," Proc. IJCAI'97 Workshop on Abduction and Induction in AI, pp.59-C3, 1997.
26. A. Yamamoto, "Theoretical foundations of inductive logic programming," The Journal of Japanese Society for Artificial Intelligence, vol.12, no.5, pp.665-674, 1997.
27. A. Yamamoto, "Which hypotheses can be found with inverse entailment?" Proc. 7th International Workshop on Inductive Logic Programming (LNAI 1297), pp.29G-308, 1997. Also in Proc. the IJCAI'97 Workshop on Frontiers of Inductive Logic Programming, pp.19-23, 1997.
28. A. Yamamoto, "An inference method for the complete inverse of relative subsumption," New Generation Computing, vol.17, no.l, pp.99-117, 1999.
29. A. Yamamoto, "Using abduction for induction based on bottom generalization," in Abductive and Inductive Reasoning : Essays on their Relation and Integration, eds. A. Kakas and P. Flach, pp.2G7-280, 1999. Hiroki Arimura was born in Fukuoka, Japan in 1965. He received the B.S. degree in 1988 in Physics, the M.S. degree in 1979 and the Ph.D. degree in 1994 in Information Systems from Kyushu University, respectively. Currently, he is an Associate Professor of the Department of Informatics, the Graduate School of Infomation Science and Electrical Engineering at Kyushu University and also a researcher of Precursory Research for Em-bryonic Science and Technology at Japan Science and Technology Corporation. His research interests include Data Mining, Computational Learning Theory, and Inductive Logic Programming. Akihiro Yamamoto was born in Kyoto, Japan in 1960. He received the D.S. degree from Kyoto University in 1985, and the M.S. and Dr.Sci. degrees from Kyushu University in 1987 and 1990 respectively. Currently, he is an Associate Professor of the Division of Electronics and Information Engineering, the Faculty of Technology at Hokkaido University, and also a researcher of Precursory Research for Embryonic Science and Technology at Japan Science and Technology Corporation. His present interests include Inductive Logic Programming, and its applications.