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Autor(en): 
  • Huaping Liu
  • Di Guo
  • Xinzhu Liu
  • Kangyao Huang
  • Embodied Multi-Agent Systems: Perception, Action, and Learning 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Mai 2025  
    Genre:  EDV / Informatik 
     
    ActivePerception / EmbodiedAgent / EmbodiedMulti-AgentSystems / FundamentalModelsforRobots / Human-RobotCollaboration / InteractiveLearninginRobotics / Künstliche Intelligenz (KI) / multi-agent
    ISBN:  9789819658701 
    EAN-Code: 
    9789819658701 
    Verlag:  Springer 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 241 mm / B 160 mm / D 20 mm 
    Gewicht:  557 gr 
    Seiten:  260 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    In recent years, embodied multi-agent systems, including multi-robots, have emerged as essential solution for demanding tasks such as search and rescue, environmental monitoring, and space exploration. Effective collaboration among these agents is crucial but presents significant challenges due to differences in morphology and capabilities, especially in heterogenous systems. While existing books address collaboration control, perception, and learning, there is a gap in focusing on active perception and interactive learning for embodied multi-agent systems. This book aims to bridge this gap by establishing a unified framework for perception and learning in embodied multi-agent systems. It presents and discusses the perception-action-learning loop, offering systematic solutions for various types of agents—homogeneous, heterogeneous, and ad hoc. Beyond the popular reinforcement learning techniques, the book provides insights into using fundamental models to tackle complex collaboration problems. By interchangeably utilizing constrained optimization, reinforcement learning, and fundamental models, this book offers a comprehensive toolkit for solving different types of embodied multi-agent problems. Readers will gain an understanding of the advantages and disadvantages of each method for various tasks. This book will be particularly valuable to graduate students and professional researchers in robotics and machine learning. It provides a robust learning framework for addressing practical challenges in embodied multi-agent systems and demonstrates the promising potential of fundamental models for scenario generation, policy learning, and planning in complex collaboration problems.

      



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