Q2 Learning and Its Application to Car Modelling

In this paper we describe an application of Q2 learning, a recently developed approach to machine learning in numerical domains (Scaronuc et al., 2003, 2004) to the automated modeling of a complex, industrially relevant mechanical system: the 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 modeling process leads to predictions that are significantly more accurate than those obtained by state-of-the-art numerical learning methods.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08839514
• Supplemental Notes:
  • Abstract reprinted with permission of Taylor and Francis.
• Authors:
  • Vladusic, D
  • Suc, D
  • Bratko, I
  • Rulka, W
• Publication Date: 2006-9

Language

• English

Media Info

• Media Type: Print
• Features: References;
• Pagination: pp 675-701
• Serial:
  • Applied Artificial Intelligence
  • Volume: 20
  • Issue Number: 8
  • Publisher: Taylor & Francis
  • ISSN: 0883-9514

Subject/Index Terms

• TRT Terms: Artificial intelligence; Automobiles; Front suspension systems; Industrial engineering; Machine learning; Mathematical models; Qualitative analysis; Rear suspension systems; Steering systems; Suspension systems
• Uncontrolled Terms: Modeling
• Subject Areas: Highways; Vehicles and Equipment; I91: Vehicle Design and Safety; I95: Vehicle Inspection;

Filing Info

• Accession Number: 01077040
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 28 2007 8:01AM