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Irregular Cellular Learning Automata.

Mehdi Esnaashari, Mohammad Reza Meybodi

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    |October 8, 2014
    PubMed
    Summary

    A new model, irregular cellular learning automaton (ICLA), extends cellular learning automaton (CLA) by removing structural regularity. This advancement is crucial for applications in computer networks and web mining, with conditions for expediency analytically determined.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Network Engineering

    Background:

    • Cellular Learning Automaton (CLA) integrates Cellular Automata (CA) and Learning Automata (LA) to adjust CA's state transition probabilities.
    • CLA has been applied to channel assignment, call admission control, image processing, and VLSI placement.
    • Existing CLA models assume structural regularity, limiting applicability in certain domains.

    Purpose of the Study:

    • Introduce an extension of CLA, termed Irregular CLA (ICLA), by relaxing the structural regularity assumption.
    • Adapt CLA for applications requiring irregular structures, such as computer networks, web mining, and grid computing.
    • Analyze the expediency of ICLA and determine conditions for its efficient operation.

    Main Methods:

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  • Developed the Irregular Cellular Learning Automaton (ICLA) model by removing the regularity constraint of standard CLA.
  • Introduced the concept of expediency specifically for the ICLA model.
  • Performed analytical derivations to establish conditions under which ICLA demonstrates expediency.

    • Main Results:

    • The ICLA model successfully accommodates applications with irregular structures.
    • Analytical conditions for ICLA expediency were derived and established.
    • The findings provide a theoretical foundation for applying ICLA in complex, irregular systems.

    Conclusions:

    • ICLA offers a more flexible and broadly applicable framework than traditional CLA.
    • The derived expediency conditions are vital for optimizing ICLA performance in practical scenarios.
    • This research advances the utility of learning automata models in dynamic and irregular computational environments.