Coordination in Multi-Agent Systems : Coordination Technology in Task-Oriented Environments

Gang Chen

ISBN 10: 3838307232 / ISBN 13: 9783838307237

Editore: LAP LAMBERT Academic Publishing, 2009

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nach der Bestellung gedruckt Neuware - Printed after ordering - Coordination, as an art of managing interdependency among activities, will be extensively studied in this book under the multi-agent system paradigm. To model the information essential to agent coordination, this book proposes a Fuzzy Subjective Task Structure (FSTS) model, through which agent coordination is viewed as a Decision-Theoretic Planning problem, to which reinforcement learning can be applied. Two learning algorithms, 'coarse-grained' and 'fine-grained' are presented to address agent coordination at two different levels. The 'coarse-grained' algorithm operates at one level and tackles hard system constraints, while the 'fine-grained' at another level and for soft constraints. Besides reinforcement learning, this book also proposes a bio-inspired approach to agent coordination. A dynamic coordination model inspired by biological metabolic system is presented. Agent coordination is achieved as every agent performs iteratively a dynamic optimization process, which utilizes explicitly the global dynamics captured through the metabolic model. All research results presented in this book are experimentally evaluated to be effective and useful in practice. Codice articolo 9783838307237

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Titolo: Coordination in Multi-Agent Systems : ...

Casa editrice: LAP LAMBERT Academic Publishing

Data di pubblicazione: 2009

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

Coordination, as an art of managing interdependency among activities, will be extensively studied in this book under the multi-agent system paradigm. To model the information essential to agent coordination, this book proposes a Fuzzy Subjective Task Structure (FSTS) model, through which agent coordination is viewed as a Decision-Theoretic Planning problem, to which reinforcement learning can be applied. Two learning algorithms, "coarse-grained" and "fine-grained" are presented to address agent coordination at two different levels. The "coarse-grained" algorithm operates at one level and tackles hard system constraints, while the "fine-grained" at another level and for soft constraints. Besides reinforcement learning, this book also proposes a bio-inspired approach to agent coordination. A dynamic coordination model inspired by biological metabolic system is presented. Agent coordination is achieved as every agent performs iteratively a dynamic optimization process, which utilizes explicitly the global dynamics captured through the metabolic model. All research results presented in this book are experimentally evaluated to be effective and useful in practice.

L'autore:

Dr. Gang Chen obtained his PhD degree from Nanyang Technological University (NTU) Singapore in 2006. He is currently a visiting assistant professor at NTU. He has research interests in various aspects of distributed systems, including multi-agent systems, peer-to-peer overlay networks, and semantic web technology.