Inductive process modeling |
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Authors: | Will Bridewell Pat Langley Ljup?o Todorovski Sa?o D?eroski |
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Affiliation: | (1) Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA 94305, USA;(2) Department of Knowledge Technologies, Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia |
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Abstract: | In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable
for scientific and engineering domains, where such notations are commonly used. We also argue that existing induction methods
are not well suited to this task, although some techniques hold partial solutions. In response, we describe an approach to
learning process models from time-series data and illustrate its behavior in three domains. In closing, we describe open issues
in process model induction and encourage other researchers to tackle this important problem.
Editor: David Page. |
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Keywords: | Scientific discovery Process models Compositional modeling System identification Ecosystem modeling |
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