Inductive Logic Programming : From Logic of Discovery to Machine Learning (Special Issue on Surveys on Discovery Science)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 2000-01-25

著者

• Arimura Hiroki

  The Graduate School Of Information Science And Electrical Engineering Kyushu University

• YAMAMOTO Akihiro

  the Faculty of Technology and Meme Media Laboratory, Hokkaido University

• Yamamoto Akihiro

  The Faculty Of Technology And Meme Media Laboratory Hokkaido University

関連論文

• Inductive Logic Programming : From Logic of Discovery to Machine Learning (Special Issue on Surveys on Discovery Science)

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