Learning Automata and Their Applications to Intelligent Systems

✍ Scribed by JunQi Zhang; MengChu Zhou

• Year: 2023
• Tongue: English
• Leaves: 275
• Category: Library

✦ Synopsis

Comprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed
Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata.
As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application.
The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested.
Written by two highly qualified academics with significant experience in the field,Learning Automata and Their Applications to Intelligent Systems covers such topics as
Mathematical analysis of the behavior of learning automata, along with suitable learning algorithms
Two application-oriented learning automata: one to discover and track spatiotemporal event patterns, and the other to solve stochastic searching on a line
Demonstrations of two pioneering variants of Optimal Computing Budge Allocation (OCBA) methods and how to combine learning automata with ordinal optimization
How to achieve significantly faster convergence and higher accuracy than classical pursuit schemes via lower computational complexity of updating the state probability
A timely text in a rapidly developing field,Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.

✦ Table of Contents

Cover
Table of Contents
Title Page
Copyright
About the Authors
Preface
Acknowledgments
A Guide to Reading this Book
Organization of the Book
1 Introduction
1.1 Ranking and Selection in Noisy Optimization
1.2 Learning Automata and Ordinal Optimization
1.3 Exercises
References
2 Learning Automata
2.1 Environment and Automaton
2.2 Fixed Structure Learning Automata
2.3 Variable Structure Learning Automata
2.4 Summary
2.5 Exercises
References
3 Fast Learning Automata
3.1 Last‐position Elimination‐based Learning Automata
3.2 Fast Discretized Pursuit Learning Automata
3.3 Exercises
References
4 Application-Oriented Learning Automata
4.1 Discovering and Tracking Spatiotemporal Event Patterns
4.2 Stochastic Searching on the Line
4.3 Fast Adaptive Search on the Line in Dual Environments
4.4 Exercises
References
5 Ordinal Optimization
5.1 Optimal Computing‐Budget Allocation
5.2 Optimal Computing‐Budget Allocation for Selection of Best and Worst Designs
5.3 Optimal Computing‐Budget Allocation for Subset Ranking
5.4 Exercises
References
6 Incorporation of Ordinal Optimization into Learning Automata
6.1 Background and Motivation
6.2 Learning Automata with Optimal Computing Budget Allocation
6.3 Proof of Optimality
6.4 Simulation Studies
6.5 Summary
6.6 Exercises
References
7 Noisy Optimization Applications
7.1 Background and Motivation
7.2 Particle Swarm Optimization
7.3 Resampling for Noisy Optimization Problems
7.4 PSO‐Based LA and OCBA
7.5 Simulations Studies
7.6 Summary
7.7 Exercises
References
8 Applications and Future Research Directions of Learning Automata
8.1 Summary of Existing Applications
8.2 Future Research Directions
8.3 Exercises
References
Index
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