Massively Multi Agent Systems Ii

This book contains revised selected and invited papers presented at the International Workshop on Massively Multi-Agent Systems, MMAS 2018, held in Stockholm, Sweden, in July 2018. The 7 revised full papers presented were carefully reviewed and selected for inclusion in this volume. Also included are 3 post-workshop papers. The papers discuss enabling technologies, new architectures, promising applications, and challenges of massively multi-agent systems in the era of IoT. They are organized in the following topical sections: multi-agent systems and Internet of Things; architectures for massively multi-agent systems; and applications of massively multi-agent systems.

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.

This book constitutes the thoroughly refereed post-workshop proceedings of the joint AAMAS 2006 International Workshops on Massively Multi-Agent Systems, MMAS 2006, and on Large scale Multi-Agent Systems, LSMAS 2006, held in Hakodate, Japan, in May 2006, and of the International Workshop on Coordination and Control in Massively Multi-Agent Systems, CCMMS 2007, held in Honolulu, HI, USA, in May 2007 as associated event of AAMAS 2007. The 13 revised full papers presented were carefully selected from the presentations made at the 3 workshops fall in 4 broad categories, presenting a snapshot of current research. Included are implementation strategies addressing coordination in the space of spatial and temporal distributed systems; approaches to deal with complexity to make decisions such as task allocation and team formation efficiently, by creating implicit or explicit encapsulations; and finally, a diverse range of applications to which these approaches may be applied, from large-scale agent based simulations to managing different types of networks to image segmentation.

In today’s world, the increasing requirement for emulating the behavior of real-world applications for achieving effective management and control has necessitated the usage of advanced computational techniques. Computational intelligence-based techniques that combine a variety of problem solvers are becoming increasingly pervasive. The ability of these methods to adapt to the dynamically changing environment and learn in an online manner has increased their usefulness in simulating intelligent behaviors as observed in humans. These intelligent systems are able to handle the stochastic and uncertain nature of the real-world problems. Application domains requiring interaction of people or organizations with different, even possibly conflicting goals and proprietary information handling are growing exponentially. To efficiently handle these types of complex interactions, distributed problem solving systems like multiagent systems have become a necessity. The rapid advancements in network communication technologies have provided the platform for successful implementation of such intelligent agent-based problem solvers. An agent can be viewed as a self-contained, concurrently executing thread of control that encapsulates some state and communicates with its environment, and possibly other agents via message passing. Agent-based systems offer advantages when independently developed components must interoperate in a heterogenous environment. Such agent-based systems are increasingly being applied in a wide range of areas including telecommunications, Business process modeling, computer games, distributed system control and robot systems.

This book presents a collection of thoroughly refereed papers drawn together from three meetings on multi-agent systems. Five of the tutorial lectures included were presented at the ACAI/EASSS 2001 summer school on MAS, held in Prague, Czech Republic, in July 2001; seven revised reviewed student papers dealing with various aspects of MAS are included as well. A workshop on Adaptability and Embodiment using MAS, AEMAS 2001, also held in Prague, Czech Republic, concurrently with the ACAI/EASSS summer school, is represented by three papers. Finally, a further nine papers were selected from an International Workshop on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2001, held in Munich, Germany, in September 2001.

This book constitutes the revised post-conference proceedings of the 16th European Conference on Multi-Agent Systems, EUMAS 2018, held at Bergen, Norway, in December 2018. The 18 full papers presented in this volume were carefully reviewed and selected from a total of 34 submissions. The papers report on both early and mature research and cover a wide range of topics in the field of multi-agent systems.

Conceptual Modelling of Multi-Agent Systems proposes the methodology and engineering environment CoMoMAS for the development of multi-agent systems. CoMoMAS is among the most elaborated and most often cited multi-agent development approaches available in the field. Its originality is to address the issue of the development of multi-agent systems (MAS) from a knowledge engineering perspective, which means that agents are seen as interacting entities having different kinds of knowledge, which is to be identified during development. Knowledge has played an important role for MAS development in the past, but CoMoMAS makes a step further in proposing a complete set of conceptual models and a solid methodology to guide the overall development process of a MAS-from design to validation. Conceptual Modelling of Multi-Agent Systems is an excellent reference for both researchers and practitioners in the broad area of distributed systems development. This book is of particular value from the point of view of computer science, including knowledge engineering, artificial intelligence, agent and multi-agent technology, and software engineering.

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems.

An introduction to multiagent systems and contemporary distributed artificial intelligence, this text provides coverage of basic topics as well as closely-related ones. It emphasizes aspects of both theory and application and includes exercises of varying degrees of difficulty.