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.

Methodological Guidelines for Modeling and Developing MAS-Based Simulations The intersection of agents, modeling, simulation, and application domains has been the subject of active research for over two decades. Although agents and simulation have been used effectively in a variety of application domains, much of the supporting research remains scattered in the literature, too often leaving scientists to develop multi-agent system (MAS) models and simulations from scratch. Multi-Agent Systems: Simulation and Applications provides an overdue review of the wide ranging facets of MAS simulation, including methodological and application-oriented guidelines. This comprehensive resource reviews two decades of research in the intersection of MAS, simulation, and different application domains. It provides scientists and developers with disciplined engineering approaches to modeling and developing MAS-based simulations. After providing an overview of the field’s history and its basic principles, as well as cataloging the various simulation engines for MAS, the book devotes three sections to current and emerging approaches and applications. Simulation for MAS — explains simulation support for agent decision making, the use of simulation for the design of self-organizing systems, the role of software architecture in simulating MAS, and the use of simulation for studying learning and stigmergic interaction. MAS for Simulation — discusses an agent-based framework for symbiotic simulation, the use of country databases and expert systems for agent-based modeling of social systems, crowd-behavior modeling, agent-based modeling and simulation of adult stem cells, and agents for traffic simulation. Tools — presents a number of representative platforms and tools for MAS and simulation, including Jason, James II, SeSAm, and RoboCup Rescue. Complete with over 200 figures and formulas, this reference book provides the necessary overview of experiences with MAS simulation and the tools needed to exploit simulation in MAS for future research in a vast array of applications including home security, computational systems biology, and traffic management.

Multi-Agent Systems are a promising technology to develop the next generation open distributed complex software systems. The main focus of the research community has been on the development of concepts (concerning both mental and social attitudes), architectures, techniques, and general approaches to the analysis and specification of multi-agent systems. This contribution has been fragmented, without any clear way of “putting it all together”, rendering it inaccessible to students and young researchers, non-experts, and practitioners. Successful multi-agent systems development is guaranteed only if we can bridge the gap from analysis and design to effective implementation. Multi-Agent Programming: Languages, Tools and Applications presents a number of mature and influential multi-agent programming languages, platforms, development tools and methodologies, and realistic applications, summarizing the state of the art in an accessible manner for professionals and computer science students at all levels.

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. 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.

Advances in networking technology have revitalized the investigation of agent technologyasapromisingparadigmforengineeringcomplexdistributedsoftware systems. Agent technology has been applied to a wide range of application - mains, including e-commerce, human-computer interfaces, telecommunications, and software assistants. Multi-agent systems (MASs) and their underlying t- ories provide a more natural support for ensuring important properties such as autonomy, mobility, environment heterogeneity, organization, openness, and intelligence. As a consequence, agent-based systems are likely to provide new - proaches to dealing with the complexity of developing and maintaining modern software. However, developing robust large-scale agent-based systems will - quire new software engineering approaches. There are currently many methods and techniques for working with individual agents or with systems built using only a few agents. Unfortunately, agent-based software engineering is still in its infancy and existing software engineering approaches are unable to cope with large MASs. The complexity associated with a large MAS is considerable. When a huge number of agents interact over heterogeneous environments, various phenomena occur which are not as easy to capture as when only a few agents are working together. As the multiple software agents are highly collaborative and operate in networked environments, they have to be context-aware and deal with - vironment uncertainty. This makes their coordination and management more di?cult and increases the likelihood of exceptional situations, such as security holes, privacy violations, and unexpected global e?ects. Moreover, as users and softwareengineersdelegatemoreautonomytotheirMASs,andputmoretrustin their results, new concerns arise in real-life applications.

Although there are plenty of publications dealing with the theory of multi-agent systems and agent-based simulations, information about the practical development of such systems is scarce. The aim of this book is to fill this empty space and to provide knowledge about design and development of agent-based simulations in an easy and comprehensible way. The book begins with the fundamentals of multi-agent systems, agent principles and their interaction, and goes on to discuss the philosophy of agent-based programming. Agent-based models - like any other scientific method - have drawbacks and limitations, which are presented in the book as well. The main portion of the text is then devoted to a description of methodology and best practices for the design and development of agent-based simulation software. The methodology (called Agentology) guides the reader through the entire development process, from the formal definition of the problem, through conceptual modeling and the selection of the particular development platform, to the programming and debugging of the code itself and the final assessment of the model. The visual language as the means of representation of the conceptual model is included. The reader is also presented with a comparison of present multi-agent development environments and tools, which could be helpful for the selection of appropriate development instruments. Given that the theoretical foundation is presented in an accessible way and supported by many practical examples, figures, schemes and source codes, this publication is especially suitable as a textbook for introductory graduate-level courses on multi-agent systems and agent-based modeling. Besides appealing to students and the scientific community, the monograph can aid software architects and developers who are not familiar with agent principles, conveying valuable insights into this distinct computer paradigm.

This book highlights new trends and challenges in agent systems, and new digital and knowledge economy research, and includes 34 papers on areas such as intelligent agent interaction and collaboration, modeling, simulation and mobile agents, agent communication and social networks, business Informatics, design and implementation of intelligent agents and multi-agent systems. These papers were presented at the 12th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2018) held on Australia’s Gold Coast. The modern economy is driven by technologies and knowledge. Digital technologies can free, shift and multiply choices, often intruding on the space of other industries, by providing new ways of conducting business operations and creating values for customers and companies. The book addresses topics that contribute to the modern digital economy, including software agents, multi-agent systems, agent modeling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computer embedded systems and nature inspired manufacturing, which contribute to the modern digital economy. The results presented are of theoretical and practical value to researchers and industrial practitioners working in the fields of artificial intelligence, collective computational intelligence, innovative business models, new digital and knowledge economy and, in particular, agent and multi-agent systems, technologies, tools and applications.

This book constitutes the thoroughly refereed postproceedings of the Joint International Workshop on Multi-Agent and Multi-Agent-Based Simulation, MABS 2004, held in New York, NY, USA in July 2004. The 20 revised full papers presented have gone through two rounds of reviewing, selection, and improvement; they present state-of-the-art research results in agent-based simulation and modeling. The papers are organized in topical sections on simulation of multi-agent systems, techniques and technologies, methodology and modeling, social dynamics, and application.