Using a Simulated Annealing to Enhance Learning in Adjustment Processes

Authors:
Albert Samà;Francisco J. Ruiz;Núria Agell;Cecilio Angulo
Affiliations:
ESADE Business School, Ramon Llull University, Barcelona, Spain and Knowledge Engineering Research Group --GREC / ESADE and UPC, Spain;Knowledge Engineering Research Group --GREC / ESADE and UPC, Spain;Knowledge Engineering Research Group --GREC / ESADE and UPC, Spain;Knowledge Engineering Research Group --GREC / ESADE and UPC, Spain
Venue:
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Year:
2009

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Abstract

This paper introduces a new approach to enhance learning in adjustment processes by using a support vector machine (SVM) algorithm as discriminant function jointly with an action generator module. The method trains a SVM with state-action patterns and uses trained SVM to select an appropriate action given a certain state in order to reach the target state. The system incorporates a simulated annealing technique to increase the exploration capacity and improve the ability to avoid local minima. The methodology has been tested in an example with artificial data.