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This book collects recent theoretical advances and concrete
applications of learning automata (LAs) in various areas of
computer science, presenting a broad treatment of the computer
science field in a survey style. Learning automata (LAs) have
proven to be effective decision-making agents, especially within
unknown stochastic environments. The book starts with a brief
explanation of LAs and their baseline variations. It subsequently
introduces readers to a number of recently developed, complex
structures used to supplement LAs, and describes their steady-state
behaviors. These complex structures have been developed because, by
design, LAs are simple units used to perform simple tasks; their
full potential can only be tapped when several interconnected LAs
cooperate to produce a group synergy. In turn, the next part of the
book highlights a range of LA-based applications in diverse
computer science domains, from wireless sensor networks, to
peer-to-peer networks, to complex social networks, and finally to
Petri nets. The book accompanies the reader on a comprehensive
journey, starting from basic concepts, continuing to recent
theoretical findings, and ending in the applications of LAs in
problems from numerous research domains. As such, the book offers a
valuable resource for all computer engineers, scientists, and
students, especially those whose work involves the reinforcement
learning and artificial intelligence domains.
This book examines the intelligent random walk algorithms based on
learning automata: these versions of random walk algorithms
gradually obtain required information from the nature of the
application to improve their efficiency. The book also describes
the corresponding applications of this type of random walk
algorithm, particularly as an efficient prediction model for
large-scale networks such as peer-to-peer and social networks. The
book opens new horizons for designing prediction models and
problem-solving methods based on intelligent random walk
algorithms, which are used for modeling and simulation in various
types of networks, including computer, social and biological
networks, and which may be employed a wide range of real-world
applications.
This book collects recent theoretical advances and concrete
applications of learning automata (LAs) in various areas of
computer science, presenting a broad treatment of the computer
science field in a survey style. Learning automata (LAs) have
proven to be effective decision-making agents, especially within
unknown stochastic environments. The book starts with a brief
explanation of LAs and their baseline variations. It subsequently
introduces readers to a number of recently developed, complex
structures used to supplement LAs, and describes their steady-state
behaviors. These complex structures have been developed because, by
design, LAs are simple units used to perform simple tasks; their
full potential can only be tapped when several interconnected LAs
cooperate to produce a group synergy. In turn, the next part of the
book highlights a range of LA-based applications in diverse
computer science domains, from wireless sensor networks, to
peer-to-peer networks, to complex social networks, and finally to
Petri nets. The book accompanies the reader on a comprehensive
journey, starting from basic concepts, continuing to recent
theoretical findings, and ending in the applications of LAs in
problems from numerous research domains. As such, the book offers a
valuable resource for all computer engineers, scientists, and
students, especially those whose work involves the reinforcement
learning and artificial intelligence domains.
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