Coordinating mobile robot group behavior using a model of interaction dynamics
Proceedings of the third annual conference on Autonomous Agents
A Probabilistic Model for Understanding and Comparing Collective Aggregation Mechansims
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
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
Learning in behavior-based multi-robot systems: policies, models, and other agents
Cognitive Systems Research
Globally Optimal Multi-agent Reinforcement Learning Parameters in Distributed Task Assignment
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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Adaptation is an essential requirement for self–organizing multi–agent systems functioning in unknown dynamic environments. Adaptation allows agents to change their actions in response to environmental changes or actions of other agents in order to improve overall system performance, and remain robust even while a sizeable fraction of agents fails. In this paper we present and study a simple model of adaptation for task allocation problem in a multi–robot system. In our model robots have to choose between two types of task, and the goal is to achieve desired task division without any explicit communication between robots. Robots estimate the state of the environment from repeated local observations and decide what task to choose based on these observations. We model robots and observations as stochastic processes and study the dynamics of individual robots and the collective behavior. We validate our analysis with numerical simulations.