Inducing Process Models from Continuous Data

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 scienti c 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 a population dynamics domain. In closing, we describe open issues in process model induction and encourage other researchers to tackle this important problem.