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Table of Contents Section I Fundamentals of Multi-Agent System Modeling Chapter 1. Towards Agent-Oriented Conceptualization and Implementation, Pratik Biswas, Avaya Labs, USA Description: This chapter provides an in-depth analysis of agent concept and compares it to conventional programming models such as object-oriented design. It defines agent models and proposes a high-level methodology for agent-oriented analysis and design. It also looks at the heart of agent-oriented programming and outlines its advantages over traditional approaches to distributed computing and interoperability. It reviews the FIPA-compliant infrastructure for building agent-based systems and suggests a Multi-agent systems framework that merges this infrastructure with the emerging J2EE technologies. Chapter 2. Concurrent Programming with Multiagents and the Chemical Abstract Machine, Wanli Ma, Dat Tran, Dharmendra Sharma, University of Canberra, Australia, and Hong Lin, University of Houston-Downtown, USA Description: A framework, MACH (Multiagent Extended Chemical Abstract Machine), for specifying autonomous agents using the chemical reaction metaphor is developed. Temporal logic is used to reason the properties of the specified system. This paper focuses on the design, implementation and verification of MACH. The aim on MACH is to develop a reactive programming language based on an interactive computational model. Chapter 3. Coalition Formation among Agents in Complex Problems Based on a Combinatorial Auction Perspective, Hiro Hattori, Massachusetts Institute of Technology, USA, Tadachika Ozono, and Toramatsu Shintani, Nagoya Institute of Technology, Japan Description: Combinatorial auction metaphor is used to develop a scheme for coordinating agents in solving complex problems. A ombinatorial auction for scheduling is formalized as an MIP (Mixed Integer Programming) problem, which integrates the constraints on items and bids to express complex problems. This integration solves the trade-off between the computation time to find the solution and the expressiveness to represent a scheduling problem. Chapter 4. A Gentle Introduction to Fuzzy Logic and Its Applications to Intelligent Agents Design, Andre de Korvin, Plamen Simeonov, and Hong Lin, University of Houston-Downtown, USA Description: This chapter presents a gentle introduction to fuzzy logic and discusses its applications to multi-agent systems. The purpose of this chapter is to present the key properties of Fuzzy Logic and Adaptive Nets and demonstrate how to use these, separately and in combination, to design intelligent systems. Section II Agent-Oriented System Design Chapter 5. Component Agent Systems: Building a Mobile Agent Architecture That You Can Reuse, Paulo Marques, and Luøs Silva, Universidade de Coimbra, Portugal Description: This chapter proposes component-based mobile agent systems to overcome the limitations of traditional platform-based approach for developing mobile agents and demonstrate the effectiveness of their method by two case studies, viz., the JAMES platform, a traditional mobile agent platform specially tailored for network management; and M&M, a component-based system for agent-enabling applications. It also presents a bird¿s eye perspective on the last 15 years of mobile agent systems research is presented along with an outlook on the future of the technology. Chapter 6. Designing a Foundation for Mobile Agents in Peer-To-Peer Networks, Daniel Lübke, Leibniz University Hannover, and Jorge Marx Gómez, Oldenburg University, Germany Description: This chapter presents an effort to develop a common platform and framework for developing mobile agents that operate on a peer-to-peer network and contain the logic of the network services. By deploying mobile agents, who can travel between network nodes, to a large P2P-network one could embrace the peer-to-peer technology and use it for all kind of services like anonymizing network traffic, distributed storage of documents, for replicating contents of heavily accessed Internet sites, trading of information etc. For many of these things there are solutions available but by using a common framework and moving the logic into the agents there is the opportunity to access all kinds of information through a common API which guarantees extensibility and widespread use. Chapter 7. Dynamic Scheduling of Multi-agent in Agent-based Distributed Network Management, Luo Junzhou, Liu Bo, Li Wei, Southeast University, China Description: This chapter develops some algorithms to support dynamic multi-agent scheduling decisions in a network management scenario. The algorithms are developed using functional decomposition strategy and an experiment has been done and shown promising results. Chapter 8. Scalable Fault Tolerant Agent Grooming Environment (SAGE), H. Farooq Ahmad, Hiroki Suguri, Communication Technologies (Comtec), Japan, Arshad Ali, Amna Basharat, Amina Tariq, NUST Institute of Information Technology, Pakistan Description: This chapter introduces a Scalable, fault tolerant Agent Grooming Environment (SAGE) for creating distributed, intelligent and autonomous entities that are encapsulated as agents. SAGE has a decentralized fault tolerant architecture that provides tools for runtime agent management, directory facilitation, monitoring and editing messages exchange between agents, and a built in mechanism to program the agent behavior and their capabilities. SAGE can be used to develop applications in a number of areas such as e-health, e-government and e-science. Chapter 9. Towards Agent-Based Grid Computing, Lizhe Wang, Hermann-von-Helmholtz-Platz 1, Germany Description: This chapter presents methodologies and technologies of agent-based Grid computing from various aspects and demonstrates that agent-based computing is a promising solution to bring a scalable, robust, thus tractable Grid. This chapter firstly reviews backgrounds for multi-agent system, agent-based computing and Grid computing. Research challenges and issues are characterized and identified together with possible solutions. After the investigation of current research efforts of agent-based Grid computing, future research trends are presented and studied. Chapter 10. MAITS: A Multiagent Based IT Security Approach, Dharmendra Sharma, Wanli Ma, Dat Tran, Shuangzhe Liu, and Mary Anderson, University of Canberra, Australia Description: This chapter studies multi-agent based IT security approach (MAITS) as a holistic solution to the increasing needs of securing computer systems. In this approach, each specialist task for security requirements is modeled as a specialist agent, which has the ability of learning, reasoning, decision making, and an agent interface that enables inter-agent communications. Section III Agent Based Intelligent Systems Chapter 11. A Methodology for Modeling Expert Knowledge for Development of Agent-Based Systems, Michael Bowman, Murray State University, USA Description: This chapter describes a methodology for modeling expert problem-solving knowledge that supports ontology import and development, teaching-based agent development, and agent-based problem solving. The methodology is applicable to a wide variety of domains and has been successfully used in the military domain. Chapter 12. Three Perspectives on Multi-agent Reinforcement Learning, Yang Gao, Hao Wang, Nanjing University, China, and Ruili Wang, Massey University, New Zealand Description: This chapter concludes three perspectives on multi-agent reinforcement learning (MARL): (i) cooperative MARL which performs mutual interaction between cooperative agents, (ii) equilibrium-based MARL which focuses on equilibrium solutions among gaming agents, and (iii) best-response MARL which suggests a no-regret policy against other competitive agents. Then, the authors present a general framework of MARL which combines all the three perspectives in order to assist readers to understand the intricate relationships between different perspectives. Furthermore, a negotiation-based MARL algorithm based on meta-equilibrium is presented which can interact with cooperative agents, games with gaming agents, and provides the best response to other competitive agents. Chapter 13. Modeling Knowledge and Reasoning in Conversational Recommendation Agents, Maria Salamó, Barry Smyth, Kevin McCarthy, James Reilly and Lorraine McGinty, UCD Dublin, Ireland Description: This chapter presents recent research of the authors on critiquing¿based recommendation and a comparison between standard and recent proposals of recommendation based on critiquing. Their work leads to conversational recommender agents which facilitate user navigation through a product space. Chapter 14. Task Allocation in Case-Based Recommender Systems: a Swarm Intelligence Approach, Fabiana Lorenzi, Daniela Scherer dos Santos, Denise de Oliveira, Ana L. C. Bazzan, Universidade Federal do Rio Grande do Sul, Brazil Description: Case-based recommender systems can learn about user preferences over time and automatically suggest products that fit these preferences. In this chapter such a system, called CASIS, is presented. In CASIS, an approach inspired by swarm intelligence is applied to a case-based recommender system in the tourism domain and experiment' results are shown that using the proposed metaphor the system always return some recommendation to the user, avoiding the user's disappointment. Section IV Applications of Multi-Agent Systems Chapter 15. A Multi-Agent System for Optimal Supply Chain Management, Hyung Rim Choi, Hyun Soo Kim, Dong-A University, Yong Sung Park, Busan Catholic University, and Byung Joo Park, Dong-A University, Korea Description: This chapter develops a multi-agent system that enables the effective formation and management of an optimal supply chain. By means of active communications among internal agents, the multi agent system for optimal supply chain management makes it possible to quickly respond to the production environment changes such as the machine failure or outage of outsourcing companies and the delivery delay of suppliers. Chapter 16. Macroscopic Modeling of Information Flow in an Agent-Based Electronic Health Record System, Ben Tse and Raman Paranjape, University of Regina, Canada Description: This chapter presents an architecture for an Agent-Based Electronic Health Record System (ABEHRS) to provide health information access and retrieval among different medical services facilities. The agent-system¿s behaviors are analyzed using the simulation approach and the mathematical modeling approach. The key concept promoted by ABEHRS is to allow patient health records to autonomously move through the computer network uniting scattered and distributed data into one consistent and complete data set or patient health record. Chapter 17. Robust Intelligent Control of Mobile Robots, Gordon Fraser, Gerald Steinbauer, Jśrg Weber, and Franz Wotawa, Graz University of Technology, Austria Description: This chapter presents a control framework which is able to control an autonomous robot in complex real-world tasks. The key features of the framework are a hybrid control paradigm which incorporates reactive-, planning- and reasoning-capabilities, a flexible software architecture, and the capability for detecting internal failures in the robot and self-healing. The framework was successfully deployed in the domain of robotic soccer and service robots. Chapter 18. RiskMan: A Multi-Agent System for Risk Management, Manolya Kavakli, Nicolas Szilas, John Porte, Iwan Kartiko, Macquarie University, Australia Description: This chapter discusses the use of multi-agent systems to develop virtual reality training systems and describe the system architecture of a multi-agent system for Risk Management (RiskMan) to help train police officers to handle high-risk situations. RiskMan has been developed using a high level scripting language of a game engine, Unreal Tournament 2004. The system integrates a Simulation Agent, Trainee Agent, Communication Agent, Interface Agent and Scripted Agents communicating using games technology.
Library of Congress Subject Headings for this publication:
Intelligent agents (Computer software).
Electronic data processing -- Distributed processing.
Computer architecture.