異種エージェント大規模チームのための統一タスク割当フレームワークSTRATA
STRATA: A Unified Framework for Task Assignments in Large Teams of Heterogeneous Agents
形質（能力）に基づいて異種エージェントを種族に分類し、連続値の形質モデルと多様性指標を用いて大規模チームのタスク割当を効果的に行う統一フレームワークを提案した。
著者: Harish Ravichandar, Kenneth Shaw, Sonia Chernova
分類: cs.RO, cs.MA
原文アブストラクト
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.
