Collaborative Research: FW-HTF: Integrating Cognitive Science and Intelligent Systems to Enhance Geoscience Practice

合作研究：FW-HTF：整合认知科学和智能系统以增强地球科学实践

• 批准号: 1839730
• 负责人: Basil Tikoff
• 金额: $ 50万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839730&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; Integrating

The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by NSF. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim. This project will make a significant contribution toward the support of future workers in geology. Understanding how geologists reason, plan to collect new data, consider three-dimensional spatial relations, and evaluate uncertainty are critically important for supporting scientists working on applied problems, such as natural resource exploration. This project will enhance existing efforts in geology to collect data using robot drones. Drones allow access to important areas of the world too dangerous to access in person and not visible from satellite or plane. The project will use machine learning to incorporate expert knowledge into drone flights to support effective autonomous data collection. The data will yield improved geological understanding of an important fault system. Findings from the project will improve understanding of uncertainty in volumes and thus improve our understanding of earthquakes and the analyses of petroleum workers. Understanding how expert geologists reason will support new exploration and mapping strategies for human-robot teams working in natural environments. The collaborative efforts of the interdisciplinary team will advance the fields of cognitive science, geology, and machine learning. The integration of cognitive science, robotics, and geology will develop new approaches to field work with human-autonomous systems teams that are faster and more effective than any either human or autonomous system would be acting alone. The project will characterize expert spatial reasoning about 3D relations and uncertainty as geologists collect data to develop a 3D understanding of a new field area, make predictions about future observations, and construct geological models. Errors in reasoning about 3D structures will be used to develop quantitative models of expert uncertainty. These models will be used to help explicitly visualize uncertainty for the experts and to construct cost functions for the robot navigation. The cost functions will include metrics that capture scientific value. The project will develop new approaches to drone exploration and mapping, including machine learning of features of interest to geologists. Drones will autonomously explore and map natural rock formations in canyon environments to support and speed up the data collection and interpretation efforts of field geologists. The project will study the structural geology of the Mecca Hills area of California, a well exposed portion of the San Andreas fault system. Robot drones will collect data about surface features to develop maps of subsurface structures. The cognitive science-infused robot design will employ successful expert strategies and focus on areas where experts are likely to make errors to prioritize exploration of those areas in navigation plans. The proposed strategies will enable 3D surface reconstruction of canyon surfaces. They will also enable better understanding of how to enhance planning and on-the-fly decision making of experts for collecting scientifically important data. The project's foundational work aims to develop an interdisciplinary understanding of how geologists build a scientific understanding of a region over time. It also aims to design autonomous exploration strategies for human-robot teams, and test new ways to support the sequential decisions about where to collect data to maximize scientific impact.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人类技术前沿的未来工作 (FW-HTF) 是 NSF 宣布的 10 个未来投资新大创意之一。 FW-HTF 跨部门计划旨在通过支持融合研究来应对不断变化的工作和工作格局的挑战和机遇。该奖项部分实现了这一目标。该项目将为支持未来的地质工作者做出重大贡献。了解地质学家如何推理、计划收集新数据、考虑三维空间关系以及评估不确定性对于支持科学家研究自然资源勘探等应用问题至关重要。该项目将加强现有的地质学工作，利用机器人无人机收集数据。 无人机可以进入世界上的重要区域，这些区域太危险而无法亲自进入，并且从卫星或飞机上看不到。 该项目将利用机器学习将专业知识融入无人机飞行中，以支持有效的自主数据收集。 这些数据将提高对重要断层系统的地质认识。该项目的研究结果将提高对体积不确定性的理解，从而提高我们对地震和石油工人分析的理解。 了解地质学家如何推理将为在自然环境中工作的人机团队提供新的勘探和绘图策略。跨学科团队的协作努力将推动认知科学、地质学和机器学习领域的发展。认知科学、机器人技术和地质学的整合将开发出与人类自治系统团队进行现场工作的新方法，这些方法比任何人类或自治系统单独行动更快、更有效。该项目将描述关于 3D 关系和不确定性的专家空间推理，因为地质学家收集数据以发展对新领域的 3D 理解、对未来观测进行预测并构建地质模型。 3D 结构推理中的错误将用于开发专家不确定性的定量模型。这些模型将用于帮助专家明确地可视化不确定性，并构建机器人导航的成本函数。成本函数将包括捕捉科学价值的指标。该项目将开发无人机勘探和测绘的新方法，包括对地质学家感兴趣的特征进行机器学习。无人机将自动探索并绘制峡谷环境中的天然岩层，以支持和加快现场地质学家的数据收集和解释工作。 该项目将研究加利福尼亚州麦加山地区的结构地质学，该地区是圣安德烈亚斯断层系统暴露良好的部分。 机器人无人机将收集有关地表特征的数据，以绘制地下结构的地图。融入认知科学的机器人设计将采用成功的专家策略，并重点关注专家可能犯错误的领域，以便在导航计划中优先探索这些领域。所提出的策略将实现峡谷表面的 3D 表面重建。它们还将使人们更好地了解如何加强专家的规划和即时决策，以收集重要的科学数据。 该项目的基础工作旨在发展跨学科的理解，了解地质学家如何随着时间的推移对一个地区建立科学的认识。它还旨在为人类-机器人团队设计自主探索策略，并测试新方法来支持有关在哪里收集数据的连续决策，以最大限度地提高科学影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Scaffolding geology content and spatial skills with playdough modeling in the field and classroom

在现场和课堂上通过橡皮泥建模搭建地质学内容和空间技能

• 发表时间: 2022-01
• 期刊: Journal of geoscience education
• 作者: Bateman; K.M.
• 通讯作者: K.M.

Explicit Instruction of Scientific Uncertainty in an Undergraduate Geoscience Field‑Based Course

本科生地球科学领域科学不确定性的明确教学——基础课程

• 发表时间: 2022-01
• 期刊: Science and Education
• 影响因子: 2.8
• 作者: Bateman, K.M.;Wilson, C.G.;Williams, R.T.;Tikoff, B.;Shipley, T.F.
• 通讯作者: Shipley, T.F.

Hit-and-run model for Cretaceous–Paleogene tectonism along the western margin of Laurentia

劳伦大陆西缘白垩纪—古近纪构造运动的肇事逃逸模型

• 发表时间: 2023-01
• 期刊: Memoir Geological Society of America
• 作者: Tikoff, B.;Housen, B.A.;Maxson, J.A.;Nelson, E.M.;Trevino, S.;and Shipley, T.F.
• 通讯作者: and Shipley, T.F.

Strategies for effective unmanned aerial vehicle use in geological field studies based on cognitive science principles

基于认知科学原理的地质野外研究中有效使用无人机的策略

• DOI: 10.1130/ges02440.1
• 发表时间: 2022-11
• 期刊: Geosphere
• 影响因子: 2.5
• 作者: Bateman, Kathryn M.;Williams, Randolph T.;Shipley, Thomas F.;Tikoff, Basil;Pavlis, Terry;Wilson, Cristina G.;Cooke, Michele L.;Fagereng, Ake
• 通讯作者: Fagereng, Ake