Coalition structure generation with worst case guarantees
Artificial Intelligence
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Local strategy learning in networked multi-agent team formation
Autonomous Agents and Multi-Agent Systems
Coalition Formation: From Software Agents to Robots
Journal of Intelligent and Robotic Systems
Distributed task allocation in social networks
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Methods for task allocation via agent coalition formation
Artificial Intelligence
RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies
Autonomous Agents and Multi-Agent Systems
Coalition formation with spatial and temporal constraints
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3
To teach or not to teach?: decision making under uncertainty in ad hoc teams
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A logic-based representation for coalitional games with externalities
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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
Multiple UAV coalition formation strategies
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
Team Formation for Generalized Tasks in Expertise Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
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
Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach
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
Resource constrained multirobot task allocation based on leader-follower coalition methodology
International Journal of Robotics Research
MMAS'04 Proceedings of the First international conference on Massively Multi-Agent Systems
Online planning for ad hoc autonomous agent teams
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Modeling and learning synergy for team formation with heterogeneous agents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Synergy graphs for configuring robot team members
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Previous approaches to select agents to form a team rely on single-agent capabilities, and team performance is treated as a sum of such known capabilities. Motivated by complex team formation situations, we address the problem where both single-agent capabilities may not be known upfront, e.g., as in ad hoc teams, and where team performance goes beyond single-agent capabilities and depends on the specific synergy among agents. We formally introduce a novel weighted synergy graph model to capture new interactions among agents. Agents are represented as vertices in the graph, and their capabilities are represented as Normally-distributed variables. The edges of the weighted graph represent how well the agents work together, i.e., their synergy in a team. We contribute a learning algorithm that learns the weighted synergy graph using observations of performance of teams of only two and three agents. Further, we contribute two team formation algorithms, one that finds the optimal team in exponential time, and one that approximates the optimal team in polynomial time. We extensively evaluate our learning algorithm, and demonstrate the expressiveness of the weighted synergy graph in a variety of problems. We show our approach in a rich ad hoc team formation problem capturing a rescue domain, namely the RoboCup Rescue domain, where simulated robots rescue civilians and put out fires in a simulated urban disaster. We show that the weighted synergy graph outperforms a competing algorithm, thus illustrating the efficacy of our model and algorithms.