Titlebook: Emergent Behavior Detection and Task Coordination for Multiagent Systems; A Distributed Estima Jing Wang Book 2022 Springer Nature Switzerl

平淡而无味

Introduction,ons, the chapter illustrates that the complicated dynamic behaviors of multiagent systems may not be well characterized by patterns of equilibrium points, such as stable/unstable nodes, saddle points, and limit cycles, and that emergent behaviors result in interaction dynamics and interaction topolo

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Gorilla

Interaction Topologies of Multiagent Systems and Consensus Algorithms,for addressing the consensus problems by focusing on the discussion on local interaction topologies and basic consensus protocols. The chapter starts with introducing the graph theory and matrix theory for describing the interaction topologies among multiagents. The conditions on interaction topolog

玉米

Emergent Behavior Detection in Multiagent Systems,d estimation algorithms for solving the problem. The objective is to detect the emergent group behaviors based only on local information exchanges among group members. The chapter begins with introducing behavior indicators for multiagent systems, and then a distributed solution for the estimation o

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2198-4182 s with nonholonomic constraints. Finally, the problem of optimal multiagent task coordination is addressed and solutions based on approximate dynamic programming and approximate distributed gradient estimation 978-3-030-86895-6978-3-030-86893-2Series ISSN 2198-4182 Series E-ISSN 2198-4190

Herpetologist

Interaction Topologies of Multiagent Systems and Consensus Algorithms,ooking into the explicit solution structures of linear multiagent systems. New cooperative backstepping control algorithms for high-order linear systems and cooperative output feedback control are designed. This chapter also discusses the impact of interaction dynamics on convergence. Specifically,

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Multiagent Distributed Optimization and Reinforcement Learning Control, group overall cost function for the multiagent system. With the use of neural network parameterization for state value functions for individual agents, a novel adaptive law for updating neural network weights is designed by using consensus-based estimation of several global terms in the algorithm.

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Emergent Behavior Detection and Task Coordination for Multiagent SystemsA Distributed Estima