Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Allocating tasks in extreme teams
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Comparing market and token-based coordination
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multi-agent role allocation: issues, approaches, and multiple perspectives
Autonomous Agents and Multi-Agent Systems
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This paper addresses the problem of multiagent task allocation in extreme teams. An extreme team is composed by a large number of agents with overlapping functionality operating in dynamic environments with possible inter-task constraints. We present eXtreme-Ants, an approximate algorithm for task allocation in extreme teams. The algorithm is inspired by the division of labor in social insects and in the process of recruitment for cooperative transport observed in ant colonies. Division of labor offers fast and efficient decision-making, while the recruitment ensures the allocation of tasks that require simultaneous execution. We compare eXtreme-Ants with two other algorithms for task allocation in extreme teams and we show that it achieves balanced efficiency regarding quality of the solution, communication, and computational effort.