Large teams of heterogeneous agents have the potential to solve complex multi-task problems that are intractable for a single agent working independently. However, solving complex multi-task problems requires leveraging the relative strengths of the different kinds of agents in the team. We present Stochastic TRAit-based Task Assignment (STRATA), a unified framework that models large teams of heterogeneous agents and performs effective task assignments. Specifically, given information on which traits (capabilities) are required for various tasks, STRATA computes the assignments of agents to tasks such that the trait requirements are achieved. Inspired by prior work in robot swarms and biodiversity, we categorize agents into different species (groups) based on their traits. We model each trait as a continuous variable and differentiate between traits that can and cannot be aggregated from different agents. STRATA is capable of reasoning about both species-level and agent-level variability in traits. Further, we define measures of diversity for any given team based on the team’s continuous-space trait model. We illustrate the necessity and effectiveness of STRATA using detailed experiments based in simulation and in a capture-the-flag game environment.

中文翻译：

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STRATA：大型异构代理团队中任务分配的统一框架

大型的异构代理团队具有解决复杂的多任务问题的潜力，这对于单个代理独立工作而言是棘手的。但是，解决复杂的多任务问题需要利用团队中不同类型的业务代表的相对优势。我们介绍了基于随机性状的任务分配（STRATA），这是一个统一的框架，可以对大型的异构代理团队进行建模并执行有效的任务分配。具体来说，给定有关各种任务需要哪些特征（功能）的信息，STRATA计算代理对任务的分配，以实现特征要求。受先前机器人群和生物多样性研究的启发，我们将特工分为不同种类（组）基于其特征。我们将每个特征建模为一个连续变量，并区分可以和不能从不同主体聚合的特征。STRATA能够对性状的物种水平和病原体水平的变异性进行推理。此外，我们根据团队的连续空间特征模型定义任何给定团队的多样性度量。我们使用基于模拟和在旗标游戏环境下的详细实验来说明STRATA的必要性和有效性。