Multi-agent and complex systems
Author(s)
Bibliographic Information
Multi-agent and complex systems
（Studies in computational intelligence, v. 670）
Springer, c2017
- : pbk
Available at / 1 libraries
-
No Libraries matched.
- Remove all filters.
Note
Softcover reprint of the hardcover 1st edition 2016
Other editors: Fenghui Ren, Katsuhide Fujita, Minjie Zhang, Takayuki Ito
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"