Innovations in Multi-Agent Systems and Application – 1

1. Front Matter

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2. An Introduction to Multi-Agent Systems

   • P. G. Balaji, D. Srinivasan

   Pages 1-27

3. Hybrid Multi-Agent Systems

   • D. Srinivasan, M. C. Choy

   Pages 29-42

4. A Framework for Coordinated Control of Multi-Agent Systems

   • Howard Li, Fakhri Karray, Otman Basir

   Pages 43-67

5. A Use of Multi-Agent Intelligent Simulator to Measure the Dynamics of US Wholesale Power Trade: A Case Study of the California Electricity Crisis

   • Toshiyuki Sueyoshi

   Pages 69-111

6. Argument Mining from RADB and Its Usage in Arguing Agents and Intelligent Tutoring System

   • Safia Abbas, Hajime Sawamura

   Pages 113-147

7. Grouping and Anti-predator Behaviors for Multi-agent Systems Based on Reinforcement Learning Scheme

   • Koichiro Morihiro, Haruhiko Nishimura, Teijiro Isokawa, Nobuyuki Matsui

   Pages 149-182

8. Multi-agent Reinforcement Learning: An Overview

   • Lucian Buşoniu, Robert Babuška, Bart De Schutter

   Pages 183-221

9. Multi-Agent Technology for Fault Tolerant and Flexible Control

   • Pavel Tichý, Raymond J. Staron

   Pages 223-246

10. Timing Agent Interactions for Efficient Agent-Based Simulation of Socio-Technical Systems

    • Seung Man Lee, Amy R. Pritchett

    Pages 247-276

11. Group-Oriented Service Provisioning in Next-Generation Network

    • Vedran Podobnik, Vedran Galetic, Krunoslav Trzec, Gordan Jezic

    Pages 277-298

12. Back Matter

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In today’s world, the increasing requirement for emulating the behavior of real-world applications for achieving effective management and control has necessitated the usage of advanced computational techniques. Computational intelligence-based techniques that combine a variety of problem solvers are becoming increasingly pervasive. The ability of these methods to adapt to the dynamically changing environment and learn in an online manner has increased their usefulness in simulating intelligent behaviors as observed in humans. These intelligent systems are able to handle the stochastic and uncertain nature of the real-world problems. Application domains requiring interaction of people or organizations with different, even possibly conflicting goals and proprietary information handling are growing exponentially. To efficiently handle these types of complex interactions, distributed problem solving systems like multiagent systems have become a necessity. The rapid advancements in network communication technologies have provided the platform for successful implementation of such intelligent agent-based problem solvers. An agent can be viewed as a self-contained, concurrently executing thread of control that encapsulates some state and communicates with its environment, and possibly other agents via message passing. Agent-based systems offer advantages when independently developed components must interoperate in a heterogenous environment. Such agent-based systems are increasingly being applied in a wide range of areas including telecommunications, Business process modeling, computer games, distributed system control and robot systems.

• agents
• architecture
• artificial intelligence
• behavior
• computational intelligence
• constraint satisfaction problem
• control
• electricity
• intelligence
• learning
• model
• multi-agent system
• problem solving
• reinforcement learning
• simulation

• Department of Electrical & Computer Engineering, National University of Singapore, Singapore

  Dipti Srinivasan

• School of Electrical and Information Engineering, University of South Australia, South Australia, Australia

  Lakhmi C. Jain

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