Technical Note: \cal Q-Learning
Machine Learning
Communication in reactive multiagent robotic systems
Autonomous Robots
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Heterogeneous multi-robot cooperation
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Proceedings of the third annual conference on Autonomous Agents
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Autonomous Robots
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A fully decentralized approach for incremental perception
Proceedings of the 1st international conference on Robot communication and coordination
Connection Science
Exploration and mapping for unstructured robot teams
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A fault-tolerant approach to robot teams
Robotics and Autonomous Systems
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This paper reviews research in three important areas concerning robot swarms: communication, diversity, and learning. Communication (or the lack of it) is a key design consideration for robot teams. Communication can enable certain types of coordination that would be impossible otherwise. However communication can also add unnecessary cost and complexity. Important research issues regarding communication concern what should be communicated, over what range, and when the communication should occur. We also consider how diverse behaviors might help or hinder a team, and how to measure diversity in the first place. Finally, we show how learning can provide a powerful means for enabling a team to master a task or adapt to changing conditions. We hypothesize that these three topics are critically interrelated in the context of learning swarms, and we suggest research directions to explore them.