Dimensions of communication and social organization in multi-agent robotic systems
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Cooperation without deliberation: a minimal behavior-based approach to multi-robot teams
Artificial Intelligence - Special issue on Robocop: the first step
An intelligent zone-based delivery scheduling approach
Computers in Industry
Solution of a Min-Max Vehicle Routing Problem
INFORMS Journal on Computing
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Web Intelligence and Agent Systems
Self-organizing social and spatial networks under what-if scenarios
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Using focal point learning to improve tactic coordination in human-machine interactions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using focal point learning to improve human---machine tacit coordination
Autonomous Agents and Multi-Agent Systems
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In the recent past, several different methods for coordinating behavior in multi-robot teams have been proposed. Common to most of them is the use of communication to coordinate behavior. For many practical applications, however, communication might not be an option (e.g., because of energy constraints of embedded platforms, limited communication range of wireless transmitters, security risks of potential interception of messages in hostile territory, etc.).In this paper we examine low-complexity, low-cost strategies without communication for coordinated agent behavior. Specifically, we investigate the utility of a "social preference mechanism" and a "pairing mechanism" in territory exploration tasks, where agents have to explore their environment to find and visit k checkpoints, which only count as "visited" when n agents are present at them at the same time. Experimental results indicate that pairing is the better strategy, raising interesting questions about tradeoffs between agent complexity and group size (e.g., whether fewer, more expensive agents with the ability to visit checkpoints individually are a better choice than more less expensive agents).