期刊名称：Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS)
印刷版ISSN：1897-8649
电子版ISSN：2080-2145
出版年度：2008
卷号：32
页码：825-877
出版社：Industrial Research Inst. for Automation and Measurements, Warsaw
摘要：Experience in the physical sciences suggests that the only realistic means of
understanding complex systems is through the use of mathematical models.
Typically, this has come to mean the identification of quantitative models
expressed as differential equations. Quantitative modelling works best when the
structure of the model (i.e., the form of the equations) is known; and the
primary concern is one of estimating the values of the parameters in the model.
For complex biological systems, the model-structure is rarely known and the
modeler has to deal with both model-identification and parameter-estimation. In
this paper we are concerned with providing automated assistance to the first of
these problems. Specifically, we examine the identification by machine of the
structural relationships between experimentally observed variables. These
relationship will be expressed in the form of qualitative abstractions of a
quantitative model. Such qualitative models may not only provide clues to the
precise quantitative model, but also assist in understanding the essence of that
model. Our position in this paper is that background knowledge incorporating
system modelling principles can be used to constrain effectively the set of good
qualitative models. Utilising the model-identification framework provided by
Inductive Logic Programming (ILP) we present empirical support for this position
using a series of increasingly complex artificial datasets. The results are
obtained with qualitative and quantitative data subject to varying amounts of
noise and different degrees of sparsity. The results also point to the presence
of a set of qualitative states, which we term kernel subsets, that may be
necessary for a qualitative model-learner to learn correct models. We
demonstrate scalability of the method to biological system modelling by
identification of the glycolysis metabolic pathway from data.
