STRATA: A Unified Framework for Task Assignments in Large Teams of Heterogeneous Agents
This addresses task assignment for complex multi-agent systems, but it is incremental as it builds on prior work in robot swarms and biodiversity.
The authors tackled the problem of assigning tasks to large teams of heterogeneous agents by developing STRATA, a framework that models agents based on traits and computes assignments to meet task requirements, showing effectiveness in simulation and a capture-the-flag game.
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.