May 14, 2024
Updated July 21, 2025
11 minute read
A Comprehensive Guide to Multi-Agent Systems
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Find a path to becoming a Multi-Agent Systems. Learn more at:
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Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Multi-Agent Systems.
Offers a rigorous and broad treatment of multi-agent systems from a computer science perspective, integrating concepts from game theory, economics, and logic. It is an excellent resource for advanced undergraduates and graduate students, providing a deeper theoretical understanding. It is often used as a textbook in academic settings.
Published in 2024, this book provides a comprehensive and up-to-date introduction to Multi-Agent Reinforcement Learning (MARL). It covers foundations, modern algorithms, and applications, making it highly relevant for those interested in contemporary topics and a valuable resource for graduate students and researchers. It includes a Python codebase for practical learning.
Is widely considered a standard introductory textbook for multi-agent systems. It provides a comprehensive overview of the field, covering key concepts, architectures, and applications. It's highly suitable for undergraduates and those new to the field, laying a solid foundation for further study.
This forthcoming book (at the time of this response, available in early access) focuses on building multi-agent systems using the AutoGen framework. It is highly relevant for those interested in practical, contemporary applications of MAS and AI agents, particularly software developers and engineers.
This book, expected in 2025, focuses on embodied multi-agent systems, particularly in robotics. It covers perception, action, and learning, offering a unified framework. It is highly relevant for graduate students and researchers in robotics and machine learning interested in physical agent systems.
Provides a comprehensive introduction to multiagent systems and distributed artificial intelligence. The second edition, published in 2013, captures developments in the field since its first publication. It is suitable for undergraduate, graduate, and postgraduate study, and can serve as a basic reference volume.
This handbook provides a comprehensive overview of research on multi-agent systems, with a focus on organizational models. It covers various perspectives and valuable reference for researchers and advanced practitioners interested in the structure and dynamics of agent organizations.
Published in 2020, this book focuses on the coordination and control of multi-agent systems, including topics like synchronization and consensus. It is particularly relevant for those interested in the control aspects of MAS and can be useful for graduate students and researchers in engineering and computer science.
Explores the application of iterative learning control (ILC) for coordinating multi-agent systems. It bridges theory and practice, showcasing applications in areas like power grids and sensor networks. It is suitable for graduate students and researchers interested in control theory applied to MAS.
Covers both the theory and applications of multi-agent systems, providing a balanced perspective. It good resource for gaining a solid understanding of the fundamental concepts and how they are applied in various domains.
Explores various aspects of autonomous agents and multi-agent systems, covering theories, methodologies, tools, and applications. It valuable resource for researchers and practitioners, offering insights into the practical aspects of building and deploying MAS.
Provides a broad introduction to multi-agent systems from a distributed artificial intelligence perspective. It covers a wide range of topics and can serve as a good starting point for understanding the scope of the field.
This monograph offers a concise yet comprehensive introduction to multiagent systems and distributed artificial intelligence. It covers theoretical foundations and recent developments, suitable for a half-semester course. It's a good option for those seeking a focused overview.
Explores agent-based computational modelling, a methodology often used in multi-agent systems research and application. It discusses technologies, methods, and challenges, providing a broader perspective on the use of agents in simulation and modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/a7lswn/multi
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