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A Mathematical Framework For Cellular Learning Automata

Author

Listed:
  • HAMID BEIGY

    (Computer Engineering Department, Sharif University of Technology, Tehran, Iran;
    Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Computer Science, Tehran, Iran)

  • M. R. MEYBODI

    (Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran;
    Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Computer Science, Tehran, Iran)

Abstract

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. The numerical results also confirm the theoretical investigations.

Suggested Citation

  • Hamid Beigy & M. R. Meybodi, 2004. "A Mathematical Framework For Cellular Learning Automata," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 7(03n04), pages 295-319.
    • Handle: RePEc:wsi:acsxxx:v:07:y:2004:i:03n04:n:s0219525904000202
    DOI: 10.1142/S0219525904000202
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    Cited by:

    1. Yaliang Wang & Xinyu Fan & Chendi Ni & Kanghong Gao & Shousong Jin, 2023. "Collaborative optimization of workshop layout and scheduling," Journal of Scheduling, Springer, vol. 26(1), pages 43-59, February.

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