This paper studies adaptive task allocation for multi-agent cooperation with unknown capabilities. The tasks considered here are single-type, large-scale and not prior known initially in every implementation. This scenario is quite common in numerous latest cooperative applications, like crowdsourcing. Since the amount of tasks are fixed, it is reasonable to assume the cost is constant, and the consumed time becomes an important index. To minimize the cost time, the main challenges lie in how to allocate tasks to complete these tasks in a decentralized way and avoid solving a new optimization problem at each step. The advantages and novelty of our work are threefold: i) Leveraging the consensus and distributed method, we transform the minimum-time problem into a solvable distributed optimization problem. By consensus algorithm, the allocation method is expressed explicitly and easily. ii) We prove that the convergence of assignment process and optimal allocation is achieved geometrically. iii) The proposed algorithm is extended to the case where the efficiencies of agents are stochastic, and simulations demonstrate the effectiveness of our approach.
Adaptive Task Allocation for Multi-agent Cooperation with Unknown Capabilities
2020-11-01
1077123 byte
Conference paper
Electronic Resource
English
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