This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.

This compendium covers several important topics related to multiagent systems, from learning and game theoretic analysis, to automated negotiation and human-agent interaction. Each chapter is written by experienced researchers working on a specific topic in mutliagent system interactions, and covers the state-of-the-art research results related to that topic.The book will be a good reference material for researchers and graduate students working in the area of artificial intelligence/machine learning, and an inspirational read for those in social science, behavioural economics and psychology.

The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The book’s main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology After reading the book the student should understand: The notion of an agent, how agents are distinct from other software paradigms (e.g. objects) and the characteristics of applications that lend themselves to agent-oriented software The key issues associated with constructing agents capable of intelligent autonomous action and the main approaches taken to developing such agents The key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systems The main application areas of agent-based systems

Mathematical modelling of systems constituted by many agents using kinetic theory is a new tool that has proved effective in predicting the emergence of collective behaviours and self-organization. This idea has been applied by the authors to various problems which range from sociology to economics and life sciences.

This compendium covers several important topics related to multiagent systems, from learning and game theoretic analysis, to automated negotiation and human-agent interaction. Each chapter is written by experienced researchers working on a specific topic in mutliagent system interactions, and covers the state-of-the-art research results related to that topic. The book will be a good reference material for researchers and graduate students working in the area of artificial intelligence/machine learning, and an inspirational read for those in social science, behavioural economics and psychology--

This book will introduce students to intelligent agents, explain what these agents are, how they are constructed and how they can be made to co-operate effectively with one another in large-scale systems.

This book helps to organise the diverse landscape of agent-based systems by applying formal methods to provide a defining and encompassing agent framework. The Z specification language is used to provide an accessible and unified formal account of agent systems and inter-agent relationships. In particular, the framework precisely and unambiguously provides meanings for common concepts and terms for agent systems, enables alternative agent models and architectures to be described within it, and provides a foundation for subsequent development of increasingly more refined agent concepts. It describes agents, the relationships between them and the requisite capabilities for effective functioning in multi-agent systems, and is applied in different case studies. In the second edition the authors have revised and updated the existing chapters of the book to respond to advice from readers of the first edition, to add references to recent work in agent systems, and generally to bring the content up to date. They have extended the introduction and conclusions chapters to include a better review of the field and the current state-of-the-art. This new edition features chapters on agent interaction and norms, and outlines an implementation framework. The book will appeal equally to researchers, students and technologists interested in intelligent agents and multi-agent systems. Comments from experts in the field: An excellent book that lays out a clear conceptual framework for studying and analysing agent-based systems. Nick Jennings Mark d'Inverno and Michael Luck have, over the last six or seven years, been at the forefront of European research in agent systems. This book poses some important foundational questions about agents and their interactions in multi-agent systems and answers them in a coherent and convincing way. It's an extremely valuable contribution to the field. Michael Georgeff It is undoubtedly a clear and most comprehensive attempt to describe agent-based systems in a unified manner. Simon Parsons

This book constitutes the joint refereed proceedings of the three confederated conferences, CoopIS 2003, DOA 2003, and ODBASE 2003, held in Catania, Sicily, Italy, in November 2003. The 95 revised full papers presented were carefully reviewed and selected from a total of 360 submissions. The papers are organized in topical sections on information integration and mediation, Web services, agent systems, cooperation and evolution, peer-to-peer systems, cooperative systems, trust management, workflow systems, information dissemination systems, data management, the Semantic Web, data mining and classification, ontology management, temporal and spatial data, data semantics and metadata, real-time systems, ubiquitous systems, adaptability and mobility, systems engineering, software engineering, and transactions.

Agents in multiagent systems are concurrent autonomous entities that need to coordinate and to cooperate so as to perform their tasks; these coordination and cooperation tasks might be achieved through communication. Communication, also called interaction by some authors, thus represents one of the major topics in multiagent systems. The state of the art of research on communication in multiagent systems is presented in this book. First, three seminal papers by Cohen and Perrault, by Singh, and by Davis and Smith present background information and introduce the newcomer to the area. The main part of the book is devoted to current research work dealing with agent communication, communication for coordination and argumentation, protocols, and dialogue games and conversational agents. Finally, the last paper deals with the future of agent communication.

Multiagent systems consist of multiple autonomous entities having different information and/or diverging interests. The study of multiagent systems (MAS) focuses on systems in which many intelligent agents interact with each other. The agents are considered to be autonomous entities, such as software programs or robots. Their interactions can be either cooperative or selfish. That is, the agents can share a common goal (e.g. an ant colony), or they can pursue their own interests. Multi-agent systems can be used to solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include some methodic, functional, procedural approach, algorithmic search or reinforcement learning. Although there is considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM). The goal of an ABM is to search for explanatory insight into the collective behavior of obeying simple rules, typically in natural systems, rather than in solving specific practical or engineering problems. Topics where multi-agent systems research may deliver an appropriate approach include online trading, disaster response, and modelling social structures. Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined humanagent teams. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains. This book, Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications, is intended to provide an emphasise on the multi-agent technology, products and industrial applications.