The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Robot Awareness in Cooperative Mobile Robot Learning
Autonomous Robots
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Collective and Cooperative Group Behaviors: Biologically Inspired Experiments in Robotics
The 4th International Symposium on Experimental Robotics IV
Interaction and Intelligent Behavior
Interaction and Intelligent Behavior
Behavioral diversity in learning robot teams
Behavioral diversity in learning robot teams
MASON: A Multiagent Simulation Environment
Simulation
Navigating a robotic swarm in an uncharted 2D landscape
Applied Soft Computing
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
A cell decomposition approach to visibility-based pursuit evasion among obstacles
International Journal of Robotics Research
Decision making as optimization in multi-robot teams
ICDCIT'12 Proceedings of the 8th international conference on Distributed Computing and Internet Technology
Agents' cooperation based on long-term reciprocal altruism
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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Multi-agent problem domains may require distributed algorithms for a variety of reasons: local sensors, limitations of communication, and availability of distributed computational resources. In the absence of these constraints, centralized algorithms are often more efficient, simply because they are able to take advantage of more information. We introduce a variant of the cooperative target observation domain which is free of such constraints. We propose two algorithms, inspired by K-means clustering and hill-climbing respectively, which are scalable in degree of decentralization. Neither algorithm consistently outperforms the other across over all problem domain settings. Surprisingly, we find that hill-climbing is sensitive to degree of decentralization, while K-means is not. We also experiment with a combination of the two algorithms which draws strength from each.