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Inductive Logic Programming (ILP) is a study of machine learning systems that use clausal theories in first-order logic as a representation language. In this paper, we survey theoretical foundations of ILP from the viewpoints of Logic of Discovery and Machine Learning, and try to unify these two views with the support of the modern theory of Logic Programming. Firstly, we define several hypothesis construction methods in ILP and give their proof-theoretic foundations by treating them as a procedure which complets incomplete proofs. Next, we discuss the design of individual learning algorithms using these hypothesis construction methods. We review known results on learning logic programs in computational learning theory, and show that these algorithms are instances of a generic learning strategy with proof completion methods.

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- IEICE TRANSACTIONS on Information Vol.E83-D No.1 pp.10-18

- Publication Date
- 2000/01/25

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- Special Section INVITED PAPER (Special Issue on Surveys on Discovery Science)

- Category

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Hiroki ARIMURA, Akihiro YAMAMOTO, "Inductive Logic Programming: From Logic of Discovery to Machine Learning" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 1, pp. 10-18, January 2000, doi: .

Abstract: Inductive Logic Programming (ILP) is a study of machine learning systems that use clausal theories in first-order logic as a representation language. In this paper, we survey theoretical foundations of ILP from the viewpoints of Logic of Discovery and Machine Learning, and try to unify these two views with the support of the modern theory of Logic Programming. Firstly, we define several hypothesis construction methods in ILP and give their proof-theoretic foundations by treating them as a procedure which complets incomplete proofs. Next, we discuss the design of individual learning algorithms using these hypothesis construction methods. We review known results on learning logic programs in computational learning theory, and show that these algorithms are instances of a generic learning strategy with proof completion methods.

URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_1_10/_p

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@ARTICLE{e83-d_1_10,

author={Hiroki ARIMURA, Akihiro YAMAMOTO, },

journal={IEICE TRANSACTIONS on Information},

title={Inductive Logic Programming: From Logic of Discovery to Machine Learning},

year={2000},

volume={E83-D},

number={1},

pages={10-18},

abstract={Inductive Logic Programming (ILP) is a study of machine learning systems that use clausal theories in first-order logic as a representation language. In this paper, we survey theoretical foundations of ILP from the viewpoints of Logic of Discovery and Machine Learning, and try to unify these two views with the support of the modern theory of Logic Programming. Firstly, we define several hypothesis construction methods in ILP and give their proof-theoretic foundations by treating them as a procedure which complets incomplete proofs. Next, we discuss the design of individual learning algorithms using these hypothesis construction methods. We review known results on learning logic programs in computational learning theory, and show that these algorithms are instances of a generic learning strategy with proof completion methods.},

keywords={},

doi={},

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month={January},}

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TY - JOUR

TI - Inductive Logic Programming: From Logic of Discovery to Machine Learning

T2 - IEICE TRANSACTIONS on Information

SP - 10

EP - 18

AU - Hiroki ARIMURA

AU - Akihiro YAMAMOTO

PY - 2000

DO -

JO - IEICE TRANSACTIONS on Information

SN -

VL - E83-D

IS - 1

JA - IEICE TRANSACTIONS on Information

Y1 - January 2000

AB - Inductive Logic Programming (ILP) is a study of machine learning systems that use clausal theories in first-order logic as a representation language. In this paper, we survey theoretical foundations of ILP from the viewpoints of Logic of Discovery and Machine Learning, and try to unify these two views with the support of the modern theory of Logic Programming. Firstly, we define several hypothesis construction methods in ILP and give their proof-theoretic foundations by treating them as a procedure which complets incomplete proofs. Next, we discuss the design of individual learning algorithms using these hypothesis construction methods. We review known results on learning logic programs in computational learning theory, and show that these algorithms are instances of a generic learning strategy with proof completion methods.

ER -