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Using a Simulated Annealing to Enhance Learning in Adjustment Processes
Albert Samà, Francisco J. Ruiz, Núria Agell, Cecilio Angulo
119 - 127
10.3233/978-1-60750-061-2-119
Frontiers in Artificial Intelligence and Applications
Volume 202: Artificial Intelligence Research and Development
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.

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