Coalition structure generation with worst case guarantees
Artificial Intelligence
Making Allocations Collectively: Iterative Group Decision Making under Uncertainty
MATES '08 Proceedings of the 6th German conference on Multiagent System Technologies
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mutual state capability-based role assignment model
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Composing near-optimal expert teams: a trade-off between skills and connectivity
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Coalition formation for task allocation: theory and algorithms
Autonomous Agents and Multi-Agent Systems
Synergy graphs for configuring robot team members
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Teaching and leading an ad hoc teammate: Collaboration without pre-coordination
Artificial Intelligence
Multi-agent team formation: diversity beats strength?
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents
Artificial Intelligence
Hi-index | 0.00 |
The performance of a team at a task depends critically on the composition of its members. There is a notion of synergy in human teams that represents how well teams work together, and we are interested in modeling synergy in multi-agent teams. We focus on the problem of team formation, i.e., selecting a subset of a group of agents in order to perform a task, where each agent has its own capabilities, and the performance of a team of agents depends on the individual agent capabilities as well as the synergistic effects among the agents. We formally define synergy and how it can be computed using a synergy graph, where the distance between two agents in the graph correlates with how well they work together. We contribute a learning algorithm that learns a synergy graph from observations of the performance of subsets of the agents, and show that our learning algorithm is capable of learning good synergy graphs without prior knowledge of the interactions of the agents or their capabilities. We also contribute an algorithm to solve the team formation problem using the learned synergy graph, and experimentally show that the team formed by our algorithm outperforms a competing algorithm.