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Learning from interpretation transition
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DOI：10.1007/s10994-013-5353-8.png)
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摘要
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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.
Keyword:
Dynamical systems
Boolean networks
Cellular automata
Attractors
Supported models
Learning from interpretation
Inductive logic programming
期刊
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2.9
论文数:
2.7K
被引数:
3.4W
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