Nonstationary models of learning automata routing in data communication networks
IEEE Transactions on Systems, Man and Cybernetics
Absorbing stochastic estimator learning automata for S-model stationary environments
Information Sciences—Informatics and Computer Science: An International Journal
Efficient fast learning automata
Information Sciences—Informatics and Computer Science: An International Journal
A new high rate adaptive wireless data dissemination scheme
Computer Communications
Group-Linking Method: A Unified Benchmark for Machine Learning with Recurrent Neural Network
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
An adaptive call admission algorithm for cellular networks
Computers and Electrical Engineering
A new traffic engineering routing algorithm for MPLS networks
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
An efficient adaptive bus arbitration scheme for scalable shared-medium ATM switch
Computer Communications
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A new class of learning automata is introduced. The new automata use a stochastic estimator and are able to operate in nonstationary environments with high accuracy and a high adaptation rate. According to the stochastic estimator scheme, the estimates of the mean rewards of actions are computed stochastically. So, they are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic estimator's contents is more relaxed when the latter are old and probably invalid. In this way, actions that have not been selected recently have the opportunity to be estimated as "optimal", to increase their choice probability, and, consequently, to be selected. Thus, the estimator is always recently updated and consequently is able to be adapted to environmental changes. The performance of the Stochastic Estimator Learning Automaton (SELA) is superior to the previous well-known S-model ergodic schemes. Furthermore, it is proved that SELA is absolutely expedient in every stationary S-model random environment.