000			03828nam a22003854u 4500
001			10.1002/9781394188536
003			Wiley
005			20251030120310.0
007			cr nn 008mam|a
008			231117t2023||||xxu fs| |0|| 0|eng||
020			_a9781394188536 _qelectronic
024	7		_a10.1002/9781394188536 _2doi
041			_aeng
100	1		_aZhang, JunQi _4aut _991698
245	1	0	_aLearning Automata and Their Applications to Intelligent Systems _h[electronic resource] _cJunQi Zhang, MengChu Zhou
250			_a1
260			_bWiley _c2023
264		1	_bWiley _c2023
300			_a272pp.;
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _2rdaft
347			_bPDF
520			_aComprehensive 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.
650		4	_aIntelligent Systems & Agents
650		4	_aArtificial Intelligence _958441
653	0	0	_aLearning automata; reinforcement learning; artificial intelligence; machine learning; machine intelligence; learning algorithms; Machine Learning; Soft Computing; Reinforcement Learning; Real-time Stochastic Optimization
700	1		_aZhou, MengChu _4aut _967265
776	0	8	_iPrinted edition: _z9781394188499
856	4	0	_uhttps://doi.org/10.1002/9781394188536 _aWiley Online Library
856	4	0	_uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781394188536 _aWiley Online Library
856	4	2	_3Cover Image _uhttps://onlinelibrary.wiley.com/cms/asset/34fd74fe-1d78-42e6-8d83-60dd3e3f90e6/9781394188536.cover.gif _aWiley Online Library
999			_c67045 _d67045