Collective iterative allocation: Enabling fast and optimal group decision makingThe role of group knowledge, optimism, and decision policies in distributed coordination

Abstract

A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. This paper extends previous work on an approach to the collective iterative allocation problem where a group of agents endeavours to find the best allocations possible through refinements of these allocations over time. For each iteration, each agent proposes an allocation based on its model of the problem domain, then one of the proposed allocations is selected and executed which enables us to assess if subsequent allocations should be refined. We offer an efficient algorithm capturing this process, and then report on theoretical and empirical results that analyse the role of three conditions in the performance of the algorithm: accuracy of agents' estimations of the performance of a task, the degree of optimism, and the type of group decision policy that determines which allocation is selected after each proposal phase.