Learning from interpretation transition

Authors:
Katsumi Inoue;Tony Ribeiro;Chiaki Sakama
Affiliations:
National Institute of Informatics, Tokyo, Japan 101-8430;Department of Informatics, The Graduate University for Advanced Studies (Sokendai), Tokyo, Japan 101-8430;Department of Computer and Communication Sciences, Wakayama University, Wakayama, Japan 640-8510
Venue:
Machine Learning
Year:
2014

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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=TP(I), where TP is the immediate consequence operator, we infer the program聽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.