A maximum likelihood Hebbian learning-based method to an agent-based architecture

Abstract

Firms need a control mechanism in order to analyse whether they are achieving their goals. A tool for the decision support process has been developed on the basis of a multi-agent system that incorporates a case-based reasoning (CBR) system and automates the business control process. The CBR system automates the organization of cases and the retrieval stage by means of a maximum likelihood Hebbian learning-based method, an extension of the principal component analysis that groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The system has been tested in 10 small and medium companies in the textile sector, located in the northwest of Spain and the results obtained have been very encouraging.

Keywords:

• agents technology
• case-based reasoning
• RBF network
• principal component analysis
• maximum likelihood Hebbian learning

2000 AMS Subject Classifications :

• 68T05
• 62B0

Acknowledgements

This research has been partially supported by the project BU006A08 of JCyL.