Abstract
The rule-generating abduction is a kind of abduction which generates a rule and proposes a hypothesis from a surprising fact. In general, there may exist infinitely many rules and hypotheses to explain such a surprising fact. Hence, we need to put some restriction on the class of rules. In rule-generating abduction, only one surprising fact is given. Hence, we also need to generalize the concept of a surprising fact. When we deal with such generalizations, we must avoid overgeneralization. It should be determined whether or not a generalization is overgeneral by an intended model. However, it is hard to give in advance such an intended model in our rule-generating abduction. Hence, in this paper we introduce a syntactical formulation of generalization, in which it can be determined whether or not a generalization is overgeneral by the forms of atoms and substitutions. On the other hand, by the restriction of rules, it suffices to consider only two types of terms, constants and lists, and two types of substitutions with these two terms. By using the above generalizations and substitutions, we design an algorithm for rule-generating abduction, which generates rules and proposes hypotheses in polynomial time with respect to the length of a surprising fact. The number of rules and hypotheses is at most the number of common terms in a surprising fact. Furthermore, we show that a common term in some argument of a surprising fact also appears in the same argument of the proposed hypothesis by this algorithm.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arimura, H., Shinohara, T., Otsuki, S.: A polynomial time algorithm for finite unions of tree pattern languages. In Proceedings of the 2nd Workshop on Algorithmic Learning Theory (1991) 105–114.
Dung, P. M.: Negation as hypothesis: an abductive foundation for logic programming. In Proceedings of the 8th International Conference on Logic Programming (1991) 3–17.
Eshghi, K., Kowalski, R. A.: Abduction compared with negation by failure. In Proceedings of the 6th International Conference on Logic Programming (1989) 234–254.
Hirata, K.: A classification of abduction: abduction of logic programming. Machine Intelligence 14 (to appear).
Hirowatari, E., Arikawa, S.: Partially isomorphic generalization and analogical reasoning. In Proceedings of European Conference on Machine Learning (1994), Lecture Notes in Artificial Intelligence 784 (1994) 363–366.
Inoue, K.: Principles of abduction. Journal of Japanese Society for Artificial Intelligence 7 (1992) 48–59 (in Japanese).
Kakas, A. C., Mancarella, P.: Generalized stable models: a semantics for abduction. In Proceedings of the 9th European Conference on Artificial Intelligence (1990) 385–391.
Kunifuji, S.: Hypothesis-based reasoning. Journal of Japanese Society for Artificial Intelligence 2 (1987) 22–87 (in Japanese).
Ling, X.: Learning and invention of Horn clause theories — a constructive method. Methodologies for Intelligent Systems 4 (1989) 323–331.
Ling, X.: Inventing theoretical terms in inductive learning of functions — search and constructive methods. Methodologies for Intelligent Systems 4 (1989) 332–341.
Lloyd, J. W.: Foundations of logic programming (second, extended edition). Springer-Verlag (1987).
Muggleton, S. (ed.): Inductive logic programming. Academic Press (1992).
Muggleton, S.: Machine invention of first-order predicates by inverting resolution. In Proceedings of the 5th International Conference on Machine Learning (1988) 339–352; In [Mug92a].
Peirce, C. S.: Collected papers of Charles Sanders Peirce (1839–1914). Hartshone, C. S., Weiss, P.(eds.), The Belknap Press (1965).
Plotkin, G. D.: A note on inductive generalization. Machine Intelligence 5 (1970) 153–163.
Plotkin, G. D.: A further note on inductive generalization. Machine Intelligence 6 (1971) 101–124.
Poole, D.: A logical framework for default reasoning. Artificial Intelligence 36 (1988) 27–47.
Shapiro, E. Y.: Inductive inference of theories from facts. Research Report 192, Yale University (1981).
Sterling, L., Shapiro, E.: The art of Prolog. The MIT Press (1986).
Yamamoto, A.: Procedural semantics and negative information of elementary formal system. Journal of Logic Programming 13 (1992) 89–97.
Yonemori, Y.: Peirce's semiotics. Keisou Syobou (1982) (in Japanese).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hirata, K. (1994). Rule-generating abduction for recursive prolog. In: Arikawa, S., Jantke, K.P. (eds) Algorithmic Learning Theory. AII ALT 1994 1994. Lecture Notes in Computer Science, vol 872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58520-6_59
Download citation
DOI: https://doi.org/10.1007/3-540-58520-6_59
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58520-6
Online ISBN: 978-3-540-49030-2
eBook Packages: Springer Book Archive