提升训练人机团队的最新技术水平：技术报告 #2 —— 研究人员知识启发调查的结果
Improving the State of the Art for Training Human-AI Teams: Technical Report #2 -- Results of Researcher Knowledge Elicitation Survey
AI总结：
本报告通过调查11名研究人员，探索人机团队训练的合成任务环境（STE）需求，发现现有开源STE已基本满足需求，并强调需自动转录、数据导出及高灵活性。
摘要：
由美国国家科学、工程与数学研究院为空军研究实验室（AFRL）编制的一份共识报告指出，各军事部门普遍且日益渴望支持人类与人工智能（AI）团队协作。Sonalysts 已启动一项内部计划，以探索人机团队的训练。该工作的第一步是开发一个能够促进人机团队研究的合成任务环境（STE）。我们的目标是创建一个 STE，提供能够支持利益相关者计划在该领域内开展的广泛研究的任务环境。因此，我们希望广泛采样相关研究界的优先事项，本报告中记录的工作是我们在这方面的初步尝试。我们创建了一项包含两类问题的调查。第一类要求受访者报告他们对预期可能重要的 STE 特征的认同度。第二类是开放式问题，要求受访者在预期 STE 的多个维度内明确其优先事项。研究团队邀请了来自学术机构和政府实验室的 19 名研究人员参与，其中 11 人完成了调查。团队分析了他们的回复，以识别出现的主题和需要进一步分析的话题。调查最显著的发现是，许多研究人员认为已经存在能够满足我们需求的各种开源 STE。研究人员还强调需要自动转录和编码工具来减轻评估团队间通信的负担；强大的数据捕获和导出能力的重要性；以及在工具许多方面具备广泛灵活性的可取性。
英文摘要：
A consensus report produced for the Air Force Research Laboratory (AFRL) by the National Academies of Sciences, Engineering, and Mathematics documented a prevalent and increasing desire to support human-Artificial Intelligence (AI) teaming across military service branches. Sonalysts has begun an internal initiative to explore the training of Human-AI teams. The first step in this effort is to develop a Synthetic Task Environment (STE) that is capable of facilitating research on Human-AI teams. Our goal is to create a STE that offers a task environment that could support the breadth of research that stakeholders plan to perform within this domain. As a result, we wanted to sample the priorities of the relevant research community broadly, and the effort documented in this report is our initial attempt to do so. We created a survey that featured two types of questions. The first asked respondents to report their agreement with STE features that we anticipated might be important. The second represented open-ended questions that asked respondents to specify their priorities within several dimensions of the anticipated STE. The research team invited nineteen researchers from academic and Government labs to participate, and 11 were able to complete the survey. The team analyzed their responses to identify themes that emerged and topics that would benefit from further analysis. The most significant finding of the survey was that a number of researchers felt that various open-source STEs that would meet our needs already exist. Researchers also emphasized the need for automated transcription and coding tools to ease the burden of assessing inter-team communications; the importance of robust data capture and export capabilities; and the desirability of extensive flexibility across many aspects of the tool.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
