Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making
Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making
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DOI：
10.1145/3623383
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发表时间：
2023-09
影响因子：
5.1
通讯作者：
Shipeng Liu;Cristina G. Wilson;Bhaskar Krishnamachari;Feifei Qian
中科院分区：
文献类型：
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作者：
Shipeng Liu;Cristina G. Wilson;Bhaskar Krishnamachari;Feifei Qian
人类科学家和自主机器人系统之间真正的协作科学领域数据收集需要对搜索目标和决策时面临的权衡有共同的理解。因此，开发智能机器人来帮助人类专家的关键是了解科学家如何做出这样的决定，以及当现场出现新信息时，他们如何调整数据收集策略。在这项研究中，我们研究了108个专家地球科学研究人员使用模拟现场场景的动态数据收集决策。人类的数据收集行为提出了两个不同的目标：一个是基于信息的目标，以最大限度地提高信息覆盖率，另一个是基于差异的目标，以最大限度地提高假设验证。我们开发了一个高度简化的定量决策模型，该模型允许机器人根据两个观察到的人类数据收集目标来预测潜在的人类数据收集位置。从简单模型的预测显示，从信息为基础的目标，以差异为基础的信息水平的增加过渡。这些发现将使机器人队友能够将专家的动态科学目标与其采样行为的适应联系起来，并从长远来看，能够开发出更具认知兼容性的机器人现场助手。
Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ. In this study, we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage and a discrepancy-based objective to maximize hypothesis verification. We developed a highly simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors and, in the long term, enable the development of more cognitively compatible robotic field assistants.