Learning Prime Implicant Conditions from Interpretation Transition

In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that $$J=T_P(I)$$ , where $$T_P$$ is the immediate consequence operator, we infer the program P. Here we propose a new learning appro...

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Bibliographic Details
Published inInductive Logic Programming Vol. 9046; pp. 108 - 125
Main Authors Ribeiro, Tony, Inoue, Katsumi
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Attractors
Boolean networks
Computer programming / software development
Dynamical systems
Inductive logic programming
Learning from interpretation
Mathematical theory of computation
Supported models
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Summary:In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that $$J=T_P(I)$$ , where $$T_P$$ is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. This new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.
Bibliography:Original Abstract: In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$J=T_P(I)$$\end{document}, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_P$$\end{document} is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. This new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.
ISBN:9783319237077
3319237071
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-23708-4_8