LEADER 04488nam 22006255 450 001 9911007462403321 005 20250521124741.0 010 $a981-9658-71-3 024 7 $a10.1007/978-981-96-5871-8 035 $a(CKB)38891412900041 035 $a(DE-He213)978-981-96-5871-8 035 $a(MiAaPQ)EBC32127059 035 $a(Au-PeEL)EBL32127059 035 $a(EXLCZ)9938891412900041 100 $a20250521d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmbodied Multi-Agent Systems $ePerception, Action, and Learning /$fby Huaping Liu, Xinzhu Liu, Kangyao Huang, Di Guo 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (XXVIII, 229 p. 108 illus., 107 illus. in color.) 225 1 $aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 311 08$a981-9658-70-5 327 $aPart I. Background -- Chapter 1. Embodied Intelligence -- Chapter 2. Embodied Multi-agent System -- Part II. Theory and Methods -- Chapter 3. Perception-Action Loop in Embodied Multi-agent System -- Chapter 4. Embodied Cooperation in Multi-agent System -- Chapter 5. Competitive Learning in Embodied Multi-agent System -- Chapter 6. Large Language Model for Embodied Multi-agent System -- Part III. Applications -- Chapter 7. Simulation Platform for Embodied Collaboration between Human and Robots -- Chapter 8. Application of Embodied Multi-agent System -- Part IV. Conlusions -- Chapter 9. Conclusions and Future Directions. 330 $aIn recent years, embodied multi-agent systems, including multi-robots, have emerged as essential solution for demanding tasks such as search and rescue, environmental monitoring, and space exploration. Effective collaboration among these agents is crucial but presents significant challenges due to differences in morphology and capabilities, especially in heterogenous systems. While existing books address collaboration control, perception, and learning, there is a gap in focusing on active perception and interactive learning for embodied multi-agent systems. This book aims to bridge this gap by establishing a unified framework for perception and learning in embodied multi-agent systems. It presents and discusses the perception-action-learning loop, offering systematic solutions for various types of agents?homogeneous, heterogeneous, and ad hoc. Beyond the popular reinforcement learning techniques, the book provides insights into using fundamental models to tackle complex collaboration problems. By interchangeably utilizing constrained optimization, reinforcement learning, and fundamental models, this book offers a comprehensive toolkit for solving different types of embodied multi-agent problems. Readers will gain an understanding of the advantages and disadvantages of each method for various tasks. This book will be particularly valuable to graduate students and professional researchers in robotics and machine learning. It provides a robust learning framework for addressing practical challenges in embodied multi-agent systems and demonstrates the promising potential of fundamental models for scenario generation, policy learning, and planning in complex collaboration problems. 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aMultiagent systems 606 $aRobotics 606 $aArtificial intelligence 606 $aMultiagent Systems 606 $aRobotics 606 $aIntelligence Infrastructure 606 $aArtificial Intelligence 615 0$aMultiagent systems. 615 0$aRobotics. 615 0$aArtificial intelligence. 615 14$aMultiagent Systems. 615 24$aRobotics. 615 24$aIntelligence Infrastructure. 615 24$aArtificial Intelligence. 676 $a006.30285436 700 $aLiu$b Huaping$4aut$4http://id.loc.gov/vocabulary/relators/aut$0954499 702 $aLiu$b Xinzhu$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHuang$b Kangyao$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGuo$b Di$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007462403321 996 $aEmbodied Multi-Agent Systems$94393316 997 $aUNINA