LEADER 05563nam 2200649Ia 450 001 9910450507503321 005 20200520144314.0 010 $a1-281-96074-8 010 $a9786611960742 010 $a981-281-184-2 035 $a(CKB)1000000000017051 035 $a(EBL)1681286 035 $a(SSID)ssj0000108018 035 $a(PQKBManifestationID)11128904 035 $a(PQKBTitleCode)TC0000108018 035 $a(PQKBWorkID)10016029 035 $a(PQKB)11249444 035 $a(MiAaPQ)EBC1681286 035 $a(WSP)00004399 035 $a(Au-PeEL)EBL1681286 035 $a(CaPaEBR)ebr10255689 035 $a(CaONFJC)MIL196074 035 $a(OCoLC)815756574 035 $a(EXLCZ)991000000000017051 100 $a20011012d2001 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAutonomous agents and multi-agent systems$b[electronic resource] $eexplorations in learning, self-organization, and adaptive computation /$fJiming Liu 210 $aSingapore ;$aRiver Edge, N.J. $cWorld Scientific$dc2001 215 $a1 online resource (300 p.) 300 $a"By request of the author, the royalty for this book goes to United Nations Children Fund (UNICEF) and a youth public welfare fund." 311 $a981-02-4282-4 320 $aIncludes bibliographic references (p. 257-276) and index. 327 $aPreface; Acknowledgements; Contents; Chapter 1 Introduction; 1.1 What is an Agent?; 1.2 Basic Questions and Fundamental Issues; 1.3 Learning; 1.3.1 Learning in Natural and Artificial Systems; 1.3.2 Agent Learning Techniques; 1.4 Neural Agents; 1.4.1 Self-Organizing Maps (SOM); 1.4.2 SOM Applications; 1.5 Evolutionary Agents; 1.6 Learning in Cooperative Agents; 1.7 Computational Architectures; 1.7.1 Subsumption Architecture; 1.7.2 Action Selection; 1.7.3 Motif Architecture; 1.8 Agent Behavioral Learning; 1.8.1 What is the Behavior of a Learning Agent?; 1.8.2 What is Behavioral Learning? 327 $aChapter 2 Behavioral Modeling, Planning, and Learning2.1 Manipulation Behaviors; 2.2 Modeling and Planning Manipulation Behaviors; 2.2.1 State-Oriented Representation; 2.2.2 State-Transition Function (?); 2.2.3 Behavioral Planning Based on Action Schemata; 2.3 Manipulation Behavioral Learning; 2.3.1 Automatic Induction of State Transitions; 2.3.2 Empirical Sample Generation; 2.4 Summary; 2.5 Other Modeling, Planning, and Learning Methods; 2.5.1 Artificial Potential Fields (APF); 2.5.2 Artificial Neural Networks (ANN); 2.5.3 Similarities and Differences between APF and ANN; 2.5.4 APF Meets ANN 327 $a2.5.5 Summary2.6 Bibliographical and Historical Remarks; 2.6.1 Assembly Operation Planning; 2.6.2 AI Planning; 2.6.3 Manipulation Behavioral Planning; Chapter 3 Synthetic Autonomy; 3.1 Synthetic Autonomy Based on Behavioral Self-Organization; 3.2 Behavioral Self-Organization; 3.2.1 Overview; 3.2.2 The Athlete Agent; 3.3 Summary; 3.4 Bibliographical and Historical Remarks; 3.4.1 Animation of Articulated Figures; 3.4.2 Lifelike Behavior; 3.4.3 Emergent Behavior; Chapter 4 Dynamics of Distributed Computation; 4.1 Definitions; 4.2 Overview of the Approach; 4.2.1 Local Stimuli to Agents 327 $a4.2.2 Reactive Behavior of Distributed Agents4.3 Dynamics of Agent-Based Distributed Search; 4.3.1 Dynamic Systems Models; 4.3.2 Agents with Different Dynamic Behaviors; 4.3.3 Summary of Agent-Based Distributed Computation; 4.4 Remarks; 4.4.1 Dynamic Systems Modeling; 4.4.2 Agent Semi-Autonomy; 4.4.3 Characteristics of the Agent-Based Approach; 4.4.4 The Goal-Attainability of Agents; 4.5 Summary; 4.5.1 Open Problems; 4.5.2 Extensions; 4.6 Bibliographical and Historical Remarks; Chapter 5 Self-Organized Autonomy in Multi-Agent Systems; 5.1 Collective Vision and Motion 327 $a5.2 Self-Organized Vision for Image Feature Detection and Tracking5.2.1 Overview of Self-Organized Vision; 5.2.2 A Two-Dimensional Lattice Environment; 5.2.3 Local Stimuli in a Two-Dimensional Lattice; 5.2.4 Self-Organizing Behaviors; 5.2.5 The Reproduce-and-Diffuse (R-D) Algorithm; 5.2.6 Examples; 5.3 Self-Organized Motion in Group Robots; 5.3.1 The Task of Group Navigation and Homing; 5.3.2 Overview of the Multi-Agent System; 5.3.3 Local Memory-Based Behavioral Selection and Global Performance-Based Behavioral Learning; 5.3.4 Dynamics of Different Agent Groups; 5.3.5 Examples; 5.3.6 Remarks 327 $a5.4 Summary 330 $aAn autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-worl 606 $aIntelligent agents (Computer software) 606 $aSelf-organizing systems 608 $aElectronic books. 615 0$aIntelligent agents (Computer software) 615 0$aSelf-organizing systems. 676 $a006.3 700 $aLiu$b Jiming$f1962-$0771474 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910450507503321 996 $aAutonomous agents and multi-agent systems$91931616 997 $aUNINA