On congestion pricing in a wireless network
Wireless Networks
Game theoretic packet relaying model for wireless ad hoc networks
International Journal of Communication Networks and Distributed Systems
The Journal of Supercomputing
An adaptive backbone formation algorithm for wireless sensor networks
Computer Communications
An adaptive learning automata-based ranking function discovery algorithm
Journal of Intelligent Information Systems
An adaptive learning to rank algorithm: Learning automata approach
Decision Support Systems
LAAP: A Learning Automata-based Adaptive Polling Scheme for Clustered Wireless Ad-Hoc Networks
Wireless Personal Communications: An International Journal
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A model is presented of learning automata playing stochastic games at two levels. The high level represents the choice of the game environment and corresponds to a group decision. The low level represents the choice of action within the selected game environment. Both of these decision processes are affected by delays in the information state due to inherent latencies or to the delayed broadcast of state changes. Analysis of the intrinsic properties of this Markov process is presented along with simulated iterative behavior and expected iterative behavior. The results show that simulation agrees with expected behavior for small step lengths in the iterative map. A Feigenbaum diagram and numerical computation of the Lyapunov exponents show that, for very small penalty parameters, the system exhibits chaotic behavior