Multi-agent and Complex Systems
(Sprache: Englisch)
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...
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Klappentext zu „Multi-agent and Complex Systems “
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.Inhaltsverzeichnis zu „Multi-agent and Complex Systems “
1.Adaptive Forwarder Selection for Distributed Wireless Sensor Networks.- 2.Trust Transference on Social Exchanges among Triads of Agents Based on Dependence Relations and Reputation.- 3.A Multiagent-Based Domain Transportation Approach for Optimal Resource Allocation in Emergency Management.- 4.A proto-type of a portable ad hoc simple water gauge and real world evaluation.- 5.Exploiting Vagueness for Multi-Agent Consensus 6.Selecting Robust Strategies Based on Abstracted Game Models.- 7.Simulating and Modeling Dual Market Segmentation Using PSA Framework.- 8.CORPNET: Towards a Decision Support System for Organizational Network Analysis using Multiplex Interpersonal Relations.- 9.Membership Function Based Matching Approach of Buyers and Sellers Through a Broker in Open E-Marketplace.- 10.The Effect of Assertiveness and Empathy on Heider's Balance Theory for Friendship Network Models information on submission.- 11.Associative Memory-based Approach to Multi-task Reinforcement Learning under Stochastic Environments.- 12.Preliminary Estimating Method of Opponent's Preferences using Simple Weighted Functions for Multi-lateral Closed Multi-issue Negotiations.- 13.Multi-Objective Nurse Rerostering Problem.- 14.Preference Aware Influence Maximization.- 15.Norm Emergence through Collective Learning and Information Diffusion in Complex Relationship Networks.- 16.Agent-Based Computation of Decomposition Games with Application in Software Requirements Decomposition.
Autoren-Porträt
Quan BaiAuckland University of TechnologyFenghui RenUniversity of WollongongMinjie ZhangUniversity of WollongongTakayuki ItoNagoya Institute of TechnologyKatsuhide FujitaTokyo University of Agriculture and TechnologyBibliographische Angaben
- 2018, Softcover reprint of the original 1st ed. 2017, VIII, 210 Seiten, 43 farbige Abbildungen, Masse: 15,9 x 23,6 cm, Kartoniert (TB), Englisch
- Herausgegeben: Quan Bai, Fenghui Ren, Katsuhide Fujita, Minjie Zhang, Takayuki Ito
- Verlag: Springer, Berlin
- ISBN-10: 981109652X
- ISBN-13: 9789811096525
Sprache:
Englisch
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