Learning in embedded systems
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An Behavior-based Robotics
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Maximizing Reward in a Non-Stationary Mobile Robot Environment
Autonomous Agents and Multi-Agent Systems
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Emergent Specialization in Swarm Systems
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2001: Robot Soccer World Cup V
Coalition Formation for Large-Scale Electronic Markets
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
An analytical and spatial model of foraging in a swarm of robots
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
A swarm robot methodology for collaborative manipulation of non-identical objects
International Journal of Robotics Research
A classification framework of adaptation in multi-agent systems
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
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Adaptation is an essential requirement for autonomous agent systems functioning in uncertain dynamic environments. Adaptation allows agents to change their behavior in order to improve the overall sys tem performance. We describe a general mechanism for adaptation in multi-agent systems in which agents modify their behavior in response to changes in the environment or actions of other agents. The agents estimate the global state of the system from local observations and adjust their actions accordingly. We derive a mathematical model that describes the collective behavior of such adaptive systems. The model, consisting of coupled rate equations, governs how the collective behavior changes in time. We apply the model to study collaboration in a group of mobile robots. The system we study is an adaptive version of the collaborative stick pulling in a group of robots examined in detail in earlier works (Ijspeert, Martinoli, Billard, & Gambardela, 2001; Lerman, Galstyan, Martinoli, & Ijspeert, 2001). In adaptive stick pulling, robots estimate the number of robots and sticks in the system and adjust their individual behavior so as to improve collective performance. We solve the mathematical model and show that adaptation improves collective performance for all parameter values.