Efficiency and Robustness of Threshold-Based Distributed Allocation Algorithms in Multi-Agent Systems

In this paper we present three scalable, fully distributed, threshold-based algorithms for allocating autonomous embodied workers to a given task whose demand evolves dynamically over time. Individuals estimate the availability of work based solely on local perceptions. The differences among the algorithms lie in the threshold distribution among teammates (homogeneous or heterogeneous team), in the mechanism used for establishing threshold values (fixed, parameter-based or variable, rule-based), and in the sharing (public) or not sharing (private) of demand estimations through local peer-to-peer communication. We tested the algorithms’ efficiency and robustness in a collective manipulation case study concerned with the clustering of initially scattered small objects. The aggregation experiment has been studied at two different experimental levels using a microscopic model and embodied simulations. Results show that teams using a number of active workers dynamically controlled by one of the allocation algorithms achieve similar or better performances in aggregation than those characterized by a constant team size while using on average a considerably reduced number of agents over the whole aggregation process. While differences in efficiency among the algorithms are small, differences in robustness are much more apparent. Threshold variability and peer-to-peer communication appear to be two key mechanisms for improving worker allocation robustness against environmental perturbations