This book constitutes the revised selected papers from the 14th European Conference on Multi-Agent Systems, EUMAS 2016, and the Fourth International Conference on Agreement Technologies, AT 2016, held in Valencia, Spain, in December 2016. The 43 papers and 2 invited papers presented in this volume were carefully reviewed and selected from 68 submissions. The papers cover thematic areas as agent and multi-agent system models, algorithms, applications, simulations, theoretical studies, and for AT the thematic areas are: algorithms
This book constitutes the revised selected papers from the 15th European Conference on Multi-Agent Systems, EUMAS 2017, and the 5th International Conference on Agreement Technologies, AT 2017, held in Evry, France, in December 2017.The 28 full papers, 3 short papers, and 2 invited papers for EUMAS and the 14 full papers and 2 short papers for AT, presented in this volume were carefully reviewed and selected from a total of 76 submissions. The papers cover thematic areas like agent-based modelling; logic and formal methods; argumentation and rational choice; simulation; games; negotiation, planning, and coalitions; algorithms and frameworks; applications; and philosophical and theoretical studies.
This book constitutes the revised selected papers from the 13 European Conference on Multi-Agent Systems, EUMAS 2015, and the Third International Conference on Agreement Technologies, AT 2015, held in Athens, Greece, in December 2015. The 36 papers presented in this volume were carefully reviewed and selected from 65 submissions. They are organized in topical sections named: coordination and planning; learning and optimization, argumentation and negotiation; norms, trust, and reputation; agent-based simulation and agent programming.
This book constitutes the revised post-conference proceedings of the 17th European Conference on Multi-Agent Systems, EUMAS 2020, and the 7th International Conference on Agreement Technologies, AT 2020, which were originally planned to be held as a joint event in Thessaloniki, Greece, in April 2020. Due to COVID-19 pandemic the conference was postponed to September 2020 and finally became a fully virtual conference. The 38 full papers presented in this volume were carefully reviewed and selected from a total of 53 submissions. The papers report on both early and mature research and cover a wide range of topics in the field of autonomous agents and multi-agent systems.
This textbook teaches control theory for multi-agent systems. Readers will learn the basics of linear algebra and graph theory, which are then developed to describe and solve multi-agent control problems. The authors address important and fundamental problems including: • consensus control; • coverage control; • formation control; • distributed optimization; and • the viral spreading phenomenon. Students' understanding of the core theory for multi-agent control is enhanced through worked examples and programs in the popular Python language. End-of-chapter exercises are provided to help assess learning progress. Instructors who adopt the book for their courses can download a solutions manual and the figures in the book for lecture slides. Additionally, the Python programs are available for download and can be used for experiments by students in advanced undergraduate or graduate courses based on this text. The broad spectrum of applications relevant to this material includes the Internet of Things, cyber-physical systems, robot swarms, communications networks, smart grids, and truck platooning. Additionally, in the spheres of social science and public health, it applies to opinion dynamics and the spreading of viruses in social networks. Students interested in learning about such applications, or in pursuing further research in multi-agent systems from a theoretical perspective, will find much to gain from Control of Multi-agent Systems. Instructors wishing to teach the subject will also find it beneficial.
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
As technology continues to become more sophisticated, a computer’s ability to understand, interpret, and manipulate natural language is also accelerating. Persistent research in the field of natural language processing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror natural language processes that have existed for centuries. Natural Language Processing: Concepts, Methodologies, Tools, and Applications is a vital reference source on the latest concepts, processes, and techniques for communication between computers and humans. Highlighting a range of topics such as machine learning, computational linguistics, and semantic analysis, this multi-volume book is ideally designed for computer engineers, computer and software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of natural language processing.
This volume constitutes the refereed proceedings of the Second International Conference on Agreement Technologies, AT 2013, held in Beijing, China, in August 2013. The 15 revised full papers presented together with two invited talks were carefully reviewed and selected from numerous submissions and focus on topics such as semantic technologies, normative multiagent systems, virtual organisations and electronic institutions, argumentation and negotiation, trust and reputation, applications of agreement technologies, agreement technologies architectures, environments and methodologies, as well as interdisciplinary foundations of agreement technologies.
This book constitutes the proceedings of the 21st International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2023, held in Guimaraes, Portugal, in July 2023. The 32 full papers in this book were reviewed and selected from 70 submissions. 5 demonstration papers are also included in this volume. The papers deal with the application and validation of agent-based models, methods, and technologies in a number of key applications areas, including: advanced models and learning, agent-based programming, decision-making, education and social interactions, formal and theoretic models, health and safety, mobility and the city, swarms and task allocation.
This book constitutes the thoroughly reviewed post-proceedings of the 7th International Workshop on Argumentation in Multi-Agent Systems, ArgMas 2010, held in Toronto, Canada in May 2010 as a satellite workshop of AAMAS 2010. The 14 revised full papers taken from ArgMAS 2010 were carefully reviewed and improved during two rounds of revision. Also included are 4 invited papers based on presentations on argumentation at the AAMAS 2010 main conference. All together the 18 papers included in the book give a representative overview on current research on argumentation in multi-agent systems. The papers are organized in topical sections on practical reasoning and argument about action, applications, and theoretical aspects.
