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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| Published in | Inductive Logic Programming Vol. 9046; pp. 108 - 125 |
|---|---|
| Main Authors | , |
| Format | Book Chapter |
| Language | English |
| Published |
Switzerland
Springer International Publishing AG
01.01.2015
Springer International Publishing |
| Series | Lecture Notes in Computer Science |
| Subjects | |
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| 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 $$J=T_P(I)$$ , 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. |
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| AbstractList | 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. |
| Author | Ribeiro, Tony Inoue, Katsumi |
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| Copyright | Springer International Publishing Switzerland 2015 |
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| DOI | 10.1007/978-3-319-23708-4_8 |
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| Discipline | Mathematics Computer Science |
| EISBN | 9783319237084 331923708X |
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| Editor | Davis, Jesse Ramon, Jan |
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| EndPage | 125 |
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| Notes | 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. |
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| PublicationSeriesSubtitle | Lecture Notes in Artificial Intelligence |
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| PublicationSubtitle | 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers |
| PublicationTitle | Inductive Logic Programming |
| PublicationYear | 2015 |
| Publisher | Springer International Publishing AG Springer International Publishing |
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| RelatedPersons | Kleinberg, Jon M. Mattern, Friedemann Naor, Moni Mitchell, John C. Terzopoulos, Demetri Steffen, Bernhard Pandu Rangan, C. Kanade, Takeo Kittler, Josef Weikum, Gerhard Hutchison, David Tygar, Doug |
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| Snippet | In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations... |
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| StartPage | 108 |
| SubjectTerms | Artificial intelligence Attractors Boolean networks Computer programming / software development Dynamical systems Inductive logic programming Learning from interpretation Mathematical theory of computation Supported models |
| Title | Learning Prime Implicant Conditions from Interpretation Transition |
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