STRATA: unified framework for task assignments in large teams of heterogeneous agents
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(2020) 34:38
STRATA: unified framework for task assignments in large teams of heterogeneous agents Harish Ravichandar1 · Kenneth Shaw1 · Sonia Chernova1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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 agentlevel 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. Keywords Multi-agent systems · Task assignment · Heterogeneous agents
1 Introduction The study of multi-agent systems has produced significant insights into the process of engineering collaborative behavior in groups of agents [6, 25]. These insights have resulted in large teams of agents capable of accomplishing complex tasks that are intractable for a single agent, with applications including environmental monitoring [31], agriculture [32], warehouse automation [36], construction [35], defense [4], and targeted drug delivery [19]. * Harish Ravichandar [email protected] Kenneth Shaw [email protected] Sonia Chernova [email protected] 1
Georgia Institute of Technology, Atlanta, GA, USA
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Autonomous Agents and Multi-Agent Systems
(2020) 34:38
Efficient solutions to the above problems typically rely on a wide range of capabilities. Teams of heterogeneous agents are particularly well suited for performing complex tasks that require a variety of skills, since they can leverage the relative advantages of the different agents and their capabilities. In this work, we are motivated by robotics applications, and the multi-robot task assignment (MRTA) problem in particular [10, 17, 18] which formally defines the challenges involved in optimally assigning agents to tasks. We present Stochastic TRAit-based Task Assignment (STRATA), a unified modeling and task assignment framework, to solve an instance of the MRTA problem with an emphasis on large heterogeneous teams. We model the topology o
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