Abstract
Learning Automata are stochastic decision-making machines that have been widely used in classification, control, and network routing, between others. Despite their versatility, one of the main drawbacks of these models is the low convergence rate of the learning rules used for the training. Estimator algorithms such as Pursuit schemes help to overcome this limitation, although they require a high computer memory cost for their operation. This fact becomes a serious inconvenient when a large set of learning automata collaborate in a team to solve a concrete task, since the memory requirements of these algorithms increases exponentially. In these cases, Pursuit algorithms are ineffective due to memory overflow.
In this work, we address this problem and we propose an estimator algorithm that can be used to train large teams of Learning Automata. The approach uses a similar strategy to Tabu Search algorithms to manage long and short term memory, in order to reduce the memory requirements. The method is applied in classic permutation problems as a test-bed.
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Cuéllar, M.P., Ros, M., Delgado, M., Vila, A. (2011). An Estimator Update Scheme for Large Teams of Learning Automata. In: Abraham, A., Corchado, J.M., González, S.R., De Paz Santana, J.F. (eds) International Symposium on Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19934-9_26
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DOI: https://doi.org/10.1007/978-3-642-19934-9_26
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