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Varieties of learning automata: an overview.
1Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India.
Summary
Learning automata (LA) models, developed since the 1960s, have seen significant theoretical and applied advancements. This paper unifies recent LA modifications and applications for broader accessibility.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Learning automata (LA) models originated in the 1960s, with foundational work by Narendra and Thathachar (1974).
- Significant theoretical and applied progress has been made in LA since their introduction.
- Recent years have seen structural modifications to LA for diverse applications.
Purpose of the Study:
- To consolidate recent advancements in learning automata theory and applications.
- To present a unified framework for understanding various LA models and their convergence properties.
- To provide comprehensive references for researchers in the field.
Main Methods:
- Review and synthesis of existing literature on learning automata.
- Analysis of modified LA structures including parameterized (PLA), generalized (GLA), and continuous action-set (CALA) models.
- Examination of group learning automata, feedforward networks, and parallel processing modules.
Main Results:
- Demonstration of convergence for various LA configurations under suitable learning algorithms.
- Identification of new LA variants (PLA, GLA, CALA) and their problem-solving capabilities.
- Highlighting the benefits of modular and parallel LA operations for faster convergence.
Conclusions:
- Recent developments in LA theory and applications are significant but scattered.
- A unified framework is crucial for understanding and advancing the field of learning automata.
- This work serves as a valuable resource for researchers exploring LA models and their applications.
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