An ant based algorithm for task allocation in large-scale and dynamic multiagent scenarios

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.