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

Gang Chen

ISBN 10: 3838307232 / ISBN 13: 9783838307237

Verlag: LAP LAMBERT Academic Publishing, 2009

Beschreibung

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. Bestandsnummer des Verkäufers 9783838307237

Bibliografische Details

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

Verlag: LAP LAMBERT Academic Publishing

Erscheinungsdatum: 2009

Einband: Taschenbuch

Zustand: Neu

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Inhaltsangabe:

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.