Learning Qualitative Models

Ivan Bratko, Dorian Suc

Abstract


In general, modeling is a complex and creative task, and building qualitative models is no exception. One way of automating this task is by means of machine learning. Observed behaviors of a modeled system are used as examples for a learning algorithm that constructs a model that is consistent with the data. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. We describe the QUIN program that looks for qualitative patterns in numeric data and outputs the results of learning as "qualitative trees." We illustrate this using applications associated with systems control, in particular, the identification and optimization of controllers and human operator's control skill. We also review approaches that learn models in terms of qualitative differential equations.

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DOI: http://dx.doi.org/10.1609/aimag.v24i4.1734

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