Engineering Multi-Agent Systems: 9th International Workshop, EMAS 2021, Virtual Event, May 3-4, 2021, Revised Selected Papers

This book is a collection of revised selected papers from the 9th International Workshop on Engineering Multi-Agent Systems,EMAS 2021,which was held online due to the COVID-19 pandemic. The papers cover various topics in agent-oriented software engineering,programming multi-agent systems,declarative agent languages,artificial intelligence,and machine learning.

Format: Paperback / softback
Length: 381 pages
Publication date: 10 March 2022
Publisher: Springer Nature Switzerland AG

This book is a compilation of revised and selected papers from the 9th International Workshop on Engineering Multi-Agent Systems (EMAS) 2021, which was originally scheduled to be held in London, UK, but was transitioned to an online format due to the COVID-19 pandemic. The conference featured a total of 27 submissions, from which 20 full papers and 1 short paper were carefully reviewed and chosen for inclusion in this volume. The contributions covered a wide range of topics, including agent-oriented software engineering, programming multi-agent systems, declarative agent languages and technologies, artificial intelligence, and machine learning.

Agent-oriented software engineering focuses on developing software systems that are designed to interact with and coordinate multiple agents, which are autonomous entities capable of performing tasks and making decisions. This field encompasses various techniques and methodologies, such as agent architecture, agent communication, and agent collaboration, to enable the development of complex and robust agent systems.

Programming multi-agent systems involves developing software agents that can collaborate and communicate with each other to achieve common goals. This involves developing languages, frameworks, and tools that facilitate the development of multi-agent systems, as well as algorithms and techniques for managing and coordinating agent behavior. Multi-agent systems are widely used in various domains, such as robotics, healthcare, and transportation, where they can improve efficiency, productivity, and decision-making.

Declarative agent languages and technologies are designed to simplify the development of agent systems by providing a high-level, intuitive syntax for specifying agent behavior. These languages allow developers to express their intentions in a more concise and readable manner, reducing the complexity of the development process. Declarative agent languages are often used in conjunction with programming languages, such as Java or Python, to develop agent systems.

Artificial intelligence (AI) is a field of computer science that focuses on developing algorithms and systems that can perform tasks that typically require human intelligence. AI encompasses a wide range of subfields, such as natural language processing, computer vision, and machine learning, and aims to develop algorithms that can learn from data, reason, and make decisions. AI has numerous applications in various domains, such as healthcare, finance, and transportation, where it can improve efficiency, accuracy, and decision-making.

Machine learning is a subfield of AI that focuses on developing algorithms that can learn from data and improve their performance over time. Machine learning algorithms are used in a wide range of applications, such as image recognition, speech recognition, and natural language processing, to enable systems to perform tasks that were previously difficult or impossible for humans to perform. Machine learning algorithms are constantly evolving and being improved, with new techniques and approaches being developed to address new challenges and improve performance.

In conclusion, this book provides a comprehensive overview of the latest research and developments in the field of engineering multi-agent systems. The contributions included in this volume cover a wide range of topics, from agent-oriented software engineering to machine learning, and offer valuable insights into the future of this rapidly evolving field.

Weight: 599g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030974565
Edition number: 1st ed. 2022