  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.
