From Tom Thumb to the Dockers: some experiments with foraging robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
The impact of diversity on performance in multi-robot foraging
Proceedings of the third annual conference on Autonomous Agents
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Pheromone-Based Utility Model for Collaborative Foraging
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Economy-like reward distribution for division of labor
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Generation of roles in reinforcement learning considering redistribution of reward between agents
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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In multiagent systems, division of labor is essential for achieving tasks. To reduce the burden of the designer, it is preferable that agents assume their role by learning. Thus, it is important to clarify the appropriate conditions under which division of labor can easily emerge. In this paper, we focus on the impact of team size on role learning. We use a simple transportation task as the test problem and investigate the impact of the team size on the learnability of division of labor. The results show that a large team size is beneficial for learning of division of labor.