Multi-agent and Complex Systems [electronic resource] /
Contributor(s): Bai, Quan [editor.] | Ren, Fenghui [editor.] | Fujita, Katsuhide [editor.] | Zhang, Minjie [editor.] | Ito, Takayuki [editor.] | SpringerLink (Online service).
Series: Studies in Computational Intelligence: 670Publisher: Singapore : Springer Singapore : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: VIII, 210 p. 73 illus., 43 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811025648.Subject(s): Computer Engineering
| Artificial Intelligence | Computational Intelligence | Information Systems and Communication Service | Organizational Studies, Economic SociologyDDC classification: 620 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: This book provides a description of advanced multi-agent and artificial intelligence technologies for the modeling and simulation of complex systems, as well as an overview of the latest scientific efforts in this field. A complex system features a large number of interacting components, whose aggregate activities are nonlinear and self-organized. A multi-agent system is a group or society of agents which interact with others cooperatively and/or competitively in order to reach their individual or common goals. Multi-agent systems are suitable for modeling and simulation of complex systems, which is difficult to accomplish using traditional computational approaches.
This book provides a description of advanced multi-agent and artificial intelligence technologies for the modeling and simulation of complex systems, as well as an overview of the latest scientific efforts in this field. A complex system features a large number of interacting components, whose aggregate activities are nonlinear and self-organized. A multi-agent system is a group or society of agents which interact with others cooperatively and/or competitively in order to reach their individual or common goals. Multi-agent systems are suitable for modeling and simulation of complex systems, which is difficult to accomplish using traditional computational approaches.
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