Learning from interpretation transition

We propose a novel 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 . The learning framework can be repeatedly applied...

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Published in:	Machine learning Vol. 94; no. 1; pp. 51 - 79
Main Authors:	Inoue, Katsumi, Ribeiro, Tony, Sakama, Chiaki
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2014 Springer Nature B.V Springer Verlag
Subjects:	Algorithms Artificial Intelligence Attraction Basins Cellular automata Computer Science Control Dynamical systems Learning Logic in Computer Science Logic programming Machine Learning Mechatronics Natural Language Processing (NLP) Robotics Simulation and Modeling Tasks Learning from interpretation Supported models Attractors Cellular automata Inductive logic programming Dynamical systems Boolean networks dynamical systems learning from interpretation Inductive Logic Programming supported models attractors cellular automata
ISSN:	0885-6125, 1573-0565
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Abstract	We propose a novel 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 . The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata.
AbstractList	We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I,J) such that J=T sub( )PI), where T sub( )Pis the immediate consequence operator, we infer the program P. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata. We propose a novel 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. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata. We propose a novel 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 . The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata. Issue Title: Special Issue on Inductive Logic Programming (ILP 2012); Guest Editors: Fabrizio Riguzzi and Filip elezný We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I,J) such that J=T ^sub P^(I), where T ^sub P^ is the immediate consequence operator, we infer the program P. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata.[PUBLICATION ABSTRACT]
Author	Ribeiro, Tony Sakama, Chiaki Inoue, Katsumi
Author_xml	– sequence: 1 givenname: Katsumi surname: Inoue fullname: Inoue, Katsumi email: inoue@nii.ac.jp organization: National Institute of Informatics – sequence: 2 givenname: Tony surname: Ribeiro fullname: Ribeiro, Tony organization: Department of Informatics, The Graduate University for Advanced Studies (Sokendai) – sequence: 3 givenname: Chiaki surname: Sakama fullname: Sakama, Chiaki organization: Department of Computer and Communication Sciences, Wakayama University
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Keywords	Learning from interpretation Supported models Attractors Cellular automata Inductive logic programming Dynamical systems Boolean networks dynamical systems learning from interpretation Inductive Logic Programming supported models attractors cellular automata
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Snippet	We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations ( I , J ) such... Issue Title: Special Issue on Inductive Logic Programming (ILP 2012); Guest Editors: Fabrizio Riguzzi and Filip elezný We propose a novel framework for... We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I,J) such that... We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that J...
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SubjectTerms	Algorithms Artificial Intelligence Attraction Basins Cellular automata Computer Science Control Dynamical systems Learning Logic in Computer Science Logic programming Machine Learning Mechatronics Natural Language Processing (NLP) Robotics Simulation and Modeling Tasks
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Title	Learning from interpretation transition
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