Based on a suitably defined coordination model distinguishing between objective (inter-agent) coordination and subjective (intra-agent) coordination, this book addresses the engineering of multi-agent systems and thus contributes to closing the gap between research and applications in agent technology. After reviewing the state of the art, the author introduces the general coordination model ECM and the corresponding object-oriented coordination language STL++. The practicability of ECM/STL++ is illustrated by the simulation of a particular collective robotics application and the automation of an e-commerce trading system. Situated at the intersection of behavior-based artificial intelligence and concurrent and distributed systems, this monograph is of relevance to the agent R&D community approaching agent technology from the distributed artificial intelligence point of view as well as for the distributed systems community.

Based on a suitably defined coordination model distinguishing between objective (inter-agent) coordination and subjective (intra-agent) coordination, this book addresses the engineering of multi-agent systems and thus contributes to closing the gap between research and applications in agent technology. After reviewing the state of the art, the author introduces the general coordination model ECM and the corresponding object-oriented coordination language STL++. The practicability of ECM/STL++ is illustrated by the simulation of a particular collective robotics application and the automation of an e-commerce trading system. Situated at the intersection of behavior-based artificial intelligence and concurrent and distributed systems, this monograph is of relevance to the agent R&D community approaching agent technology from the distributed artificial intelligence point of view as well as for the distributed systems community.

Challenges arise when the size of a group of cooperating agents is scaled to hundreds or thousands of members. In domains such as space exploration, military and disaster response, groups of this size (or larger) are required to achieve extremely complex, distributed goals. To effectively and efficiently achieve their goals, members of a group need to cohesively follow a joint course of action while remaining flexible to unforeseen developments in the environment. Coordination of Large-Scale Multiagent Systems provides extensive coverage of the latest research and novel solutions being developed in the field. It describes specific systems, such as SERSE and WIZER, as well as general approaches based on game theory, optimization and other more theoretical frameworks. It will be of interest to researchers in academia and industry, as well as advanced-level students.

1. 1 Background In this work, we develop a framework for the design of multi-agent systems inspired by (human) organizational principles. Organizations are complex entities formed to ov- come various limitations of individual agencies, such as cognitive, physical, temporal and institutional limitations. There is a parallel between the complexity of organizations and multi-agent systems. Therefore, we explore the use of concepts, methods and techniques from human organizational design as architectural principles for multi-agent systems. Three research lines are presented: organizational modeling and coordination, interop- ability and agent models. Organizational modeling and coordination are concerned with how resources (i. e. agents) can be identi?ed and related to each other. In order to have agents cooperate, several issues of interoperability have to be addressed. Agent models deal with the design of individual intelligent software agents, taking into account typical features of agent intelligence. Every (human) activity raises two challenges: division of labor and coordi- tion [Mintzberg, 1993]. Division of labor is the decomposition of work (or goals) into various distinct tasks. Coordination refers to managing relations between these tasks to carry out the work. The patterns of division of labor, responsibilities (people who do the work), clustering of responsibilities into units and coordination between units can be de?ned by organizational structures [Galbraith, 1973]. The design of an organization should cover how one or more actors are engaged in one or more tasks, where knowledge, capabilities and resources are distributed.

An Application Science For Multi-Agent Systems addresses the complexity of choosing which multi-agent control technologies are appropriate for a given problem domain or a given application. Without such knowledge, when faced with a new application domain, agent developers must rely on past experience and intuition to determine whether a multi-agent system is the right approach, and if so, how to structure the agents, how to decompose the problem, and how to coordinate the activities of the agents, and so forth. This unique collection of contributions, written by leading international researchers in the agent community, provides valuable insight into the issues of deciding which technique to apply and when it is appropriate to use them. The contributions also discuss potential trade-offs or caveats involved with each decision. An Application Science For Multi-Agent Systems is an excellent reference for anyone involved in developing multi-agent systems.

Nowadays, engineering large-scale software systems means dealing with complex systems composed of pervasive software components that move around and adapt to nondeterministic and open environments, like the Internet, in order to achieve systems design goals through the coordination of autonomously distributed services. The agent metaphor, in particular software agents and multi-agent systems (MAS), constitutes a promising approach for covering most of the software development life cycle, from conceptual modeling and requirements specification to architectural definition, design, and implementation. This book presents 17 carefully reviewed papers arranged in order to provide a coherent survey of how to exploit agent properties and MAS issues in today's software systems. The book offers the following topical sections: - software engineering foundations - requirements engineering and software architecture - coordination and mobility - reuse -dependability -empirical studies and applications

This book presents 10 chapters on various aspects of intelligent information agents contributed by members of the respective AgentLink special interest group. The papers are organized in three parts on agent-based information systems, adaptive information agents, and coordination of information agents. Also included are a comprehensive introduction and surveys for each of the three parts.

"This book provide a comprehensive view of current developments in agent organizations as a paradigm for both the modeling of human organizations, and for designing effective artificial organizations"--Provided by publisher.

Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.