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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Vydáno v:	Machine learning Ročník 94; číslo 1; s. 51 - 79
Hlavní autoři:	Inoue, Katsumi, Ribeiro, Tony, Sakama, Chiaki
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.01.2014 Springer Nature B.V Springer Verlag
Témata:	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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Cites_doi	10.1007/s10994-011-5259-2 10.1016/0304-3975(92)90019-C 10.1073/pnas.0305937101 10.1007/BF03037383 10.1016/j.artint.2008.03.005 10.1016/j.jal.2008.10.001 10.1145/321978.321991 10.1007/s10994-006-8988-x 10.1016/S0004-3702(97)00041-6 10.1016/S0304-3975(02)00425-5 10.1109/TC.1986.1676819 10.1093/bioinformatics/18.2.261 10.1007/978-3-642-22359-4_17 10.1007/s10994-013-5341-z 10.1023/A:1007694015589 10.1145/1055686.1055687 10.1023/A:1018913302060 10.1093/bioinformatics/btn287 10.1093/bioinformatics/btn336 10.1093/oso/9780195079517.001.0001 10.1109/TCBB.2010.20 10.1093/bioinformatics/bti664 10.1016/B978-0-934613-40-8.50006-3 10.1007/s00344-006-0068-8 10.1093/bioinformatics/btl210 10.1007/BF03037168 10.7551/mitpress/3897.001.0001 10.1371/journal.pone.0001672 10.1145/321250.321253 10.1007/978-3-642-30743-0_23
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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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