Multi-agent system
Related papers: 20
About
A multi-agent system (MAS) is a computational framework composed of multiple autonomous agents — software programs, robots, or other intelligent entities — that perceive their environment, make independent decisions, and interact with one another to accomplish individual or collective goals. In robotics and AI, MAS are used to coordinate teams of robots for tasks such as formation control, search and rescue, distributed sensing, and cooperative manipulation, where agents communicate locally or operate without direct communication to achieve emergent group behavior. Agents may be homogeneous or heterogeneous, and their interactions can be cooperative, competitive, or mixed, depending on the application. Multi-agent reinforcement learning extends this framework by enabling agents to learn effective joint policies through experience. MAS matter because they offer scalability, robustness, and parallelism that single-agent approaches cannot match — if one agent fails, others continue operating. They also enable solutions to problems too complex or spatially distributed for any individual agent, making them foundational to modern autonomous systems, swarm robotics, and human-autonomy teaming applications.
Top Researchers
Top Institutes
Top Cited Papers
An Introduction to MultiAgent Systems
Michael Wooldridge
Citations: 5185 • 2002
An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination
Yongcan Cao, Wenwu Yu, Wei Ren, Guanrong Chen
Citations: 2385 • 2012
A Comprehensive Survey of Multiagent Reinforcement Learning
Lucian Buşoniu, Robert Babuška, Bart De Schutter
Citations: 2178 • 2008
Distributed multi‐vehicle coordinated control<i>via</i>local information exchange
Wei Ren, Ella Atkins
Citations: 1478 • 2006
Cooperative Multi-Agent Learning: The State of the Art
Liviu Panait, Sean Luke
Citations: 1250 • 2005
Multiagent Systems: A Survey from a Machine Learning Perspective
Peter Stone, Manuela Veloso
Citations: 1188 • 2000
MASON: A Multiagent Simulation Environment
Sean Luke, Claudio Cioffi‐Revilla, Liviu Panait, Keith Sullivan, Gabriel Balan
Citations: 1007 • 2005
Multi-agent Reinforcement Learning: An Overview
Lucian Buşoniu, Robert Babuška, Bart De Schutter
Citations: 746 • 2010
Remote Agent: to boldly go where no AI system has gone before
Nicola Muscettola, P. Pandurang Nayak, Barney Pell, Brian C. Williams
Citations: 700 • 1998
Cooperative Control of Multi-Agent Systems: Optimal and Adaptive Design Approaches
Frank L. Lewis, Hongwei Zhang, Kristian Hengster‐Movric, Abhijit Das
Citations: 602 • 2013
Formation Control and Collision Avoidance for Multi-agent Non-holonomic Systems: Theory and Experiments
Silvia Mastellone, Dušan M. Stipanović, Christopher R. Graunke, Koji A. Intlekofer, Mark W. Spong
Citations: 450 • 2007
Cooperative Heterogeneous Multi-Robot Systems
Yara Rizk, Mariette Awad, Edward Tunstel
Citations: 416 • 2019
A taxonomy for multi-agent robotics
Gregory Dudek, Michael Jenkin, Evangelos Milios, D. Wilkes
Citations: 402 • 1996
A control Lyapunov function approach to multiagent coordination
Petter Ögren, Magnus Egerstedt, Xiaoming Hu
Citations: 388 • 2002
Secure Cooperative Event-Triggered Control of Linear Multiagent Systems Under DoS Attacks
Zhi Feng, Guoqiang Hu
Citations: 385 • 2019
Layered Learning in Multiagent Systems
Peter Stone
Citations: 381 • 2000
Cooperative Control of Distributed Multi‐Agent Systems
Citations: 362 • 2007
Cooperative Control of Multiple Nonholonomic Mobile Agents
Wenjie Dong, Jay A. Farrell
Citations: 345 • 2008
Situation awareness-based agent transparency and human-autonomy teaming effectiveness
Jessie Y. C. Chen, Shan Lakhmani, Kimberly Stowers, Anthony R. Selkowitz, Julia L. Wright, Michael Barnes
Citations: 327 • 2018
Introducing the tileworld: experimentally evaluating agent architectures
Martha E. Pollack, Marc Ringuette
Citations: 316 • 1990