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Towards the Integration of Inductive and Nonmonotonic Logic Programming

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Book cover Progress in Discovery Science

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2281))

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Abstract

Commonsense reasoning and machine learning are two important topics in AI. These techniques are realized in logic programming as nonmonotonic logic programming (NMLP) and inductive logic programming (ILP), respectively. NMLP and ILP have seemingly different motivations and goals, but they have much in common in the background of problems. This article overviews the author’s recent research results for realizing induction from nonmonotonic logic programs.

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References

  1. M. Bain and S. Muggleton. Non-monotonic learning. In: S. Muggleton (ed.), Inductive Logic Programming, Academic Press, pp. 145–161, 1992.

    Google Scholar 

  2. C. Baral and M. Gelfond. Logic programming and knowledge representation. Journal of Logic Programming 19/20:73–148, 1994.

    Article  MathSciNet  Google Scholar 

  3. F. Bergadano, D. Gunetti, M. Nicosia, and G. Ruffo. Learning logic programs with negation as failure. In: L. De Raedt (ed.), Advances in Inductive Logic Programming, IOS Press, pp. 107–123, 1996.

    Google Scholar 

  4. G. Brewka and J. Dix. Knowledge representation withlogic programs. In: Proc. 3rd Workshop on Logic Programming and Knowledge Representation, Lecture Notes in Artificial Intelligence 1471, Springer-Verlag, pp. 1–51, 1997.

    Google Scholar 

  5. Y. Dimopoulos and A. Kakas. Learning nonmonotonic logic programs: learning exceptions. In: Proc. 8th European Conf. on Machine Learning, Lecture Notes in Artificial Intelligence 912, Springer-Verlag, pp. 122–137, 1995.

    Google Scholar 

  6. L. Fogel and G. Zaverucha. Normal programs and multiple predicate learning. In: Proc. 8th Int’l Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence 1446, Springer-Verlag, pp. 175–184, 1998.

    Google Scholar 

  7. M. Gelfond and V. Lifschitz. The stable model semantics for logic programming. In: Proc. 5th Int’l Conf. and Symp. on Logic Programming, MIT Press, pp. 1070–1080, 1988.

    Google Scholar 

  8. M. Gelfond and V. Lifschitz. Classical negation in logic programs and disjunctive databases. New Generation Computing 9:365–385, 1991.

    Article  Google Scholar 

  9. K. Inoue and Y. Kudoh. Learning extended logic programs. In: Proc. 15th Int’l Joint Conf. on Artificial Intelligence, Morgan Kaufmann, pp. 176–181, 1997.

    Google Scholar 

  10. E. Lamma, F. Riguzzi, and L. M. Pereira. Strategies in combined learning via logic programs. Machine Learning 38(1/2), pp. 63–87, 2000.

    Article  MATH  Google Scholar 

  11. L. Martin and C. Vrain. A three-valued framework for the induction of general logic programs. In: L. De Raedt (ed.), Advances in Inductive Logic Programming, IOS Press, pp. 219–235, 1996.

    Google Scholar 

  12. S. Muggleton (ed.). Inductive Logic Programming, Academic Press, 1992.

    Google Scholar 

  13. S. Muggleton and W. Buntine. Machine invention of first-order predicate by inverting resolution. In: [12], pp. 261–280, 1992.

    Google Scholar 

  14. S. Muggleton. Inverse entailment and Progol. New Generation Computing 13:245–286, 1995.

    Google Scholar 

  15. S. Muggleton. Completing inverse entailment. In: Proc. 8th Int’l Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence 1446, Springer-Verlag, pp. 245–249, 1998.

    Google Scholar 

  16. S.-H. Nienhuys-Cheng and R. deWolf. Foundations of inductive logic programming. Lecture Notes in Artificial Intelligence 1228, Springer-Verlag, 1997.

    Google Scholar 

  17. C. Sakama. Some properties of inverse resolution in normal logic programs. In: Proc. 9th Int’l Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence 1634, Springer-Verlag, pp. 279–290, 1999.

    Google Scholar 

  18. C. Sakama. Inverse entailment in nonmonotonic logic programs. In: Proc. 10th Int’l Conf. on Inductive Logic Programming, Lecture Notes in Artificial Intelligence 1866, Springer-Verlag, pp. 209–224, 2000.

    Google Scholar 

  19. C. Sakama. Learning by answer sets. In: Proc. AAAI Spring Symp. on Answer Set Programming, AAAI Press, pp. 181–187, 2001.

    Google Scholar 

  20. J. Seitzer. Stable ILP: exploring the added expressivity of negation in the background knowledge. In: Proc. IJCAI-95 Workshop on Frontiers of ILP, 1997.

    Google Scholar 

  21. Y. Shoham. Nonmonotonic logics: meaning and utility. In: Proc. 10th Int’l Joint Conf. on Artificial Intelligence, Morgan Kaufmann, pp. 388–393, 1987.

    Google Scholar 

  22. K. Taylor. Inverse resolution of normal clauses. In: Proc. 3rd Int’l Workshop on Inductive Logic Programming, J. Stefan Institute, pp. 165–177, 1993.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer and Communication Sciences, Wakayama University, Sakaedani, 640 8510, Wakayama, Japan

    Chiaki Sakama

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  1. Chiaki Sakama
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Editor information

Editors and Affiliations

  1. Department of Informatics, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, 812-8581, Fukuoka, Japan

    Setsuo Arikawa  & Ayumi Shinohara  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Sakama, C. (2002). Towards the Integration of Inductive and Nonmonotonic Logic Programming. In: Arikawa, S., Shinohara, A. (eds) Progress in Discovery Science. Lecture Notes in Computer Science(), vol 2281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45884-0_10

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  • DOI: https://doi.org/10.1007/3-540-45884-0_10

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  • Print ISBN: 978-3-540-43338-5

  • Online ISBN: 978-3-540-45884-5

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