Multiagent systems (MAS) are one of the most exciting and the fastest growing domains in the intelligent resource management and agent-oriented technology, which deals with modeling of autonomous decisions making entities. Recent developments have produced very encouraging results in the novel approach of handling multiplayer interactive systems. In particular, the multiagent system approach is adapted to model, control, manage or test the operations and management of several system applications including multi-vehicles, microgrids, multi-robots, where agents represent individual entities in the network. Each participant is modeled as an autonomous participant with independent strategies and responses to outcomes. They are able to operate autonomously and interact pro-actively with their environment. In recent works, the problem of information consensus is addressed, where a team of vehicles communicate with each other to agree on key pieces of information that enable them to work together in a coordinated fashion. The problem is challenging because communication channels have limited range and there are possibilities of fading and dropout. The book comprises chapters on synchronization and consensus in multiagent systems. It shows that the joint presentation of synchronization and consensus enables readers to learn about similarities and differences of both concepts. It reviews the cooperative control of multi-agent dynamical systems interconnected by a communication network topology. Using the terminology of cooperative control, each system is endowed with its own state variable and dynamics. A fundamental problem in multi-agent dynamical systems on networks is the design of distributed protocols that guarantee consensus or synchronization in the sense that the states of all the systems reach the same value. It is evident from the results that research in multiagent systems offer opportunities for further developments in theoretical, simulation and implementations. This book attempts to fill this gap and aims at presenting a comprehensive volume that documents theoretical aspects and practical applications.

This is the first comprehensive introduction to multiagent systems and contemporary distributed artificial intelligence that is suitable as a textbook.

Since its conception almost 30 years ago, the BDI (Belief Desire Intention) model of agency has become established, along with Soar, as the approach of choice for practitioners in the development of knowledge intensive agent applications. However, in developing BDI agent applications for over 15 years, the authors of this book have observed a disconnect between what the BDI model provides and what is actually required of an agent model in order to build practical systems. The GORITE BDI framework was developed to address this gap and this book is written for students, researchers and practitioners who wish to gain a practical understanding of how GORITE is used to develop BDI agent applications. In this regard, a feature of the book is the use of complete, annotated examples. As GORITE is a Java framework, a familiarity with Java (or a similar language) is assumed, but no prior knowledge of the BDI model is required.

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.

This book features a selection of best papers from 13 workshops held at the International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, held in Sao Paulo, Brazil, in May 2017. The 17 full papers presented in this volume were carefully reviewed and selected for inclusion in this volume. They cover specific topics, both theoretical and applied, in the general area of autonomous agents and multiagent systems.

As intelligent autonomous agents and multiagent system applications become more pervasive, it becomes increasingly important to understand the risks associated with using these systems. Incorrect or inappropriate agent behavior can have harmful - fects, including financial cost, loss of data, and injury to humans or systems. For - ample, NASA has proposed missions where multiagent systems, working in space or on other planets, will need to do their own reasoning about safety issues that concern not only themselves but also that of their mission. Likewise, industry is interested in agent systems that can search for new supply opportunities and engage in (semi-) automated negotiations over new supply contracts. These systems should be able to securely negotiate such arrangements and decide which credentials can be requested and which credentials may be disclosed. Such systems may encounter environments that are only partially understood and where they must learn for themselves which aspects of their environment are safe and which are dangerous. Thus, security and safety are two central issues when developing and deploying such systems. We refer to a multiagent system’s security as the ability of the system to deal with threats that are intentionally caused by other intelligent agents and/or s- tems, and the system’s safety as its ability to deal with any other threats to its goals.

Advanced Distributed Consensus for Multiagent Systems contributes to the further development of advanced distributed consensus methods for different classes of multiagent methods. The book expands the field of coordinated multiagent dynamic systems, including discussions on swarms, multi-vehicle and swarm robotics. In addition, it addresses advanced distributed methods for the important topic of multiagent systems, with a goal of providing a high-level treatment of consensus to different versions while preserving systematic analysis of the material and providing an accounting to math development in a unified way. This book is suitable for graduate courses in electrical, mechanical and computer science departments. Consensus control in multiagent systems is becoming increasingly popular among researchers due to its applicability in analyzing and designing coordination behaviors among agents in multiagent frameworks. Multiagent systems have been a fascinating subject amongst researchers as their practical applications span multiple fields ranging from robotics, control theory, systems biology, evolutionary biology, power systems, social and political systems to mention a few. - Gathers together the theoretical preliminaries and fundamental issues related to multiagent systems and controls - Provides coherent results on adopting a multiagent framework for critically examining problems in smart microgrid systems - Presents advanced analysis of multiagent systems under cyberphysical attacks and develops resilient control strategies to guarantee safe operation

More and more, software systems involve autonomous and distributed software components that have to execute and interact in open and dynamic environments, such as in pervasive, autonomous, and mobile applications. The requirements with respect to dynamics, openness, scalability, and decentralization call for new approaches to software design and development, capable of supporting spontaneous configuration, tolerating partial failures, or arranging adaptive reorganization of the whole system. Inspired by the behaviour of complex natural systems, scientists and engineers have started to adjust their mechanisms and techniques for self-organization and adaption to changing environments. In line with these considerations, Mamei and Zambonelli propose an interaction model inspired by the way masses and particles in our universe move and self-organize according to contextual information represented by gravitational and electromagnetic fields. The key idea is to have the components’ actions driven by computational force fields, generated by the components themselves or by some infrastructures, and propagated across the environment. Together with its supporting middleware infrastructure – available with additional information under http://www.agentgroup.unimore.it – this model can serve as the basis for a general purpose and widely applicable approach for the design and development of adaptive distributed applications.

The increasing complexity of manufacturing systems as well as the overall demands for flexible and fault-tolerant control of production processes stimulates (among many others) two key emerging technologies that are already making an important breakthrough in the field of intelligent manufacturing, control, and diagnostics. These two paradigms are: • the holonic approach based on the event-driven control strategy, usually aimed at modular control systems that are directly physically linked with the manufacturing hardware equipment, and • the multi-agent approach developed in the area of distributed information processing. The research communities working in both these fields are approaching the problem of intelligent manufacturing from different viewpoints and, until recently, to a certain extent, in an independent way. We can however observe quite a clear convergence of these fields in the last few years: the communities have started to cooperate, joining efforts to solve the painful problems involved in achieving effective industrial practice. We can see convergence in the terminology, standards and methods being applied.