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.

Varieties of Cellular Learning Automata: An overview.- Cellular learning automata: A bibliometric analysis.- Learning from multiple reinforcements in cellular learning automata.- Applications of cellular learning automata and reinforcement learning in global optimization.- Applications of multi-reinforcement cellular learning automata in channel assignment.- Cellular Learning Automata for Collaborative Loss Sharing.- Cellular Learning Automata for Competitive Loss Sharing.- Cellular Learning Automata versus Multi-Agent Reinforcement Learning.