This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA's parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments. The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.
This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA's parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments. The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.
Cellular Automata Transforms describes a new approach to using the dynamical system, popularly known as cellular automata (CA), as a tool for conducting transforms on data. Cellular automata ...
This book explores Probabilistic Cellular Automata (PCA) from the perspectives of statistical mechanics, probability theory, computational biology and computer science. PCA are extensions of the...
Are mathematical equations the best way to model nature? For many years it had been assumed that they were. But in the early 1980s, Stephen Wolfram made the radical proposal that one should instead...
Cellular automata were introduced in the first half of the last century by John von Neumann who used them as theoretical models for self-reproducing machines. The authors present a self-contained...
This book presents an extensive survey and report of related research on important developments in cellular automata (CA) theory. The authors introduce you to this theory in a comprehensive manner...
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