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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 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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