期刊
MACHINE LEARNING
卷 94, 期 1, 页码 51-79出版社
SPRINGER
DOI: 10.1007/s10994-013-5353-8
关键词
Dynamical systems; Boolean networks; Cellular automata; Attractors; Supported models; Learning from interpretation; Inductive logic programming
资金
- NII research project on Dynamic Constraint Networks
- Systems Resilience project at Research Organization of Information and Systems, Japan
- Grants-in-Aid for Scientific Research [24500174] Funding Source: KAKEN
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据