Abstract
The paper describes recent results from developing and testing LUS methodology for user modeling. LUS employs AQ learning for automatically creating user models from datasets representing activities of computer users. The datasets are stored in a relational database and employed in the learning process through an SQL-style command that automatically executes the AQ20 rule learning program and generates user models. The models are in the form of attributional rulesets that are more expressive than conventional decision rules, and are easy to interpret and understand. Early experimental results from the testing of the LUS method gave highly encouraging results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bloedorn, E. and Michalski, R.S., “Data Driven Constructive Induction in AQ17-PRE: A Method and Experiments,” Proceedings of the Third International Conference on Tools for AI, San Jose, CA, November 9–14, 1991.
Cervone G., Panait L. A., Michalski R.S, Michalski R.S., “The Development of the AQ20 Learning System and Initial Experiments,” Proceedings of the International Conference on Intelligent Systems (IIS 2000), Poland, July 2001.
Michalski, R.S. and Kaufman, K., “Building Knowledge Scouts Using KGL Metalanguage,” Fundamenta Informaticae 40, pp. 433–447, 2000a.
Kaufman, K.A. and Michalski, R.S., An Adjustable Rule Learner for Pattern Discovery Using the AQ Methodology,“ Journal of Intelligent Information Systems, 14, pp. 199–216, 2000b.
Michalski, R.S, “A Theory and Methodology of Inductive Learning, in Machine Learning: An Artificial Intelligence Approach, Michalski, R.S, Carbonell, J.G. and Mitchell, T.M. ( Eds. ), Tioga Publishing Company, 1983, pp. 83–134.
Michalski, R.S., and Chilausky, R.L., “Learning By Being Told and Learning From Examples: An Experimental Comparison of the Two Methods of Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis,” Policy Analysis and Information Systems, Vol. 4, No. 2, 1980.
Wnek, J. and Michalski, R.S, R.S., “Hypothesis-Driven Constructive Induction in AQ17: A Method and Experiments,” Reports of the Machine Learning and Inference Laboratory, MLI 91–4, School of Information Technology and Engineering, George Mason University, Fairfax, VA, May 1991.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cervone, G., Michalski, R.S. (2002). Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results. In: Kłopotek, M.A., Wierzchoń, S.T., Michalewicz, M. (eds) Intelligent Information Systems 2002. Advances in Soft Computing, vol 17. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1777-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1777-5_5
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1509-2
Online ISBN: 978-3-7908-1777-5
eBook Packages: Springer Book Archive