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Coordination, as an art of managing interdependency among activities, will be extensively studied in this book under the multiagent 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 DecisionTheoretic Planning problem, to which reinforcement learning can be applied. Two learning algorithms, "coarsegrained" and "finegrained" are presented to address agent coordination at two different levels. The "coarsegrained" algorithm operates at one level and tackles hard system constraints, while the "finegrained" at another level and for soft constraints. Besides reinforcement learning, this book also proposes a bioinspired 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.
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