Inductive process modeling

  • Authors:

  • Will Bridewell;Pat Langley;Ljupčo Todorovski;Sašo Džeroski

  • Affiliations:

  • Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, USA 94305;Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, USA 94305;Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia 1000;Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia 1000

  • Venue:
  • Machine Learning
  • Year:
  • 2008

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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.