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Hum.-Robot Interact."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>\n            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\n            <jats:italic>in situ<\/jats:italic>\n            . 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\u2019 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.\n          <\/jats:p>","DOI":"10.1145\/3623383","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T12:18:27Z","timestamp":1694607507000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0524-5085","authenticated-orcid":false,"given":"Shipeng","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Southern California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9639-2752","authenticated-orcid":false,"given":"Cristina G.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Collaborative Robotics and Intelligent Systems Institute, Oregon State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9994-9931","authenticated-orcid":false,"given":"Bhaskar","family":"Krishnamachari","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Southern California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9656-1168","authenticated-orcid":false,"given":"Feifei","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Southern California, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103500"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2011.12.010"},{"key":"e_1_3_2_4_2","first-page":"3811","volume-title":"Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201917)","author":"Candela Alberto","year":"2017","unstructured":"Alberto Candela, David Thompson, Eldar Noe Dobrea, and David Wettergreen. 2017. 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