Coalition structure generation with worst case guarantees
Artificial Intelligence
Distributed task allocation in social networks
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Coalition structure generation in multi-agent systems with positive and negative externalities
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Coalition structure generation in multi-agent systems with mixed externalities
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Constant factor approximation algorithms for coalition structure generation
Autonomous Agents and Multi-Agent Systems
Empirical evaluation of ad hoc teamwork in the pursuit domain
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Modeling and learning synergy for team formation with heterogeneous agents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents
Artificial Intelligence
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Robots are becoming increasingly modular in their design, allowing different configurations of hardware and software, e.g., different wheels, sensors, and algorithms. We are interested in forming a multi-robot team by configuring each robot (i.e., selecting the different modules) to best fit a task. This general problem is applicable to many domains, such as manufacturing in high-mix low-volume scenarios. In this paper, we formally define the Synergy Graph for Configurable Robots (SGraCR) model, where each robot module is modeled as a vertex in a graph, and we define how to compute the synergy of modules within a single robot, as well as between robots, using the structure of the graph. We define the synergy of a multi-robot team comprised of such configurable robots, and contribute a team formation algorithm that searches a SGraCR to approximate the optimal team. In addition, we contribute a learning algorithm that learns a SGraCR from a small set of training data containing the performance of teams. We evaluate our SGraCR model and algorithm in extensive experiments, both in simulation and with real robots, and compare with competing algorithms.