Learning from interpretation transition

Katsumi Inoue

doi:10.1007/s10994-013-5353-8

Learning from interpretation transition

2013-05-01

50

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AI

DOI：10.1007/s10994-013-5353-8

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Abstract

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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.

Keywords:

Dynamical systems

Boolean networks

Cellular automata

Attractors

Supported models

Learning from interpretation

Inductive logic programming

Learning from interpretation transition

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