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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Veröffentlicht in:Machine learning Jg. 94; H. 1; S. 51 - 79
Hauptverfasser: Inoue, Katsumi, Ribeiro, Tony, Sakama, Chiaki
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2014
Springer Nature B.V
Springer Verlag
Schlagworte:
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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Beschreibung
Zusammenfassung: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.
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-013-5353-8