Q 2 LEARNING AND ITS APPLICATION TO CAR MODELLING

Authors: Vladušič, D.¹; Šuc, D.¹; Bratko, I.¹; Rulka, W.²

Source: Applied Artificial Intelligence, Volume 20, Number 8, September 2006 , pp. 675-701(27)

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Abstract:

In this paper we describe an application of Q 2 learning, a recently developed approach to machine learning in numerical domains (Šuc et al., 20032004) to the automated modelling of a complex, industrially relevant mechanical system - a four wheel suspension and steering system of a car. In this experiment, first a qualitative model of this dynamic system was induced from data, and then this model was reified into a quantitative model. The induced qualitative models enable explanation of relations among the variables in the system and, when reified into quantitative models, enable accurate numerical prediction. Furthermore, the qualitative guidance of the quantitative modelling process leads to predictions that are significantly more accurate than those obtained by state-of-the-art numerical learning methods.

Document Type: Research article

DOI: http://dx.doi.org/10.1080/08839510600847238

Affiliations: 1: Faculty of Computer and Information Science, University of Ljubljana, Tržaška, Ljubljana, Slovenia 2: Intec GmbH, Argelsrieder Feld, Wessling, Germany

Publication date: 2006-09-01

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