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Title: Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making
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 informationin 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.  more » « less
Award ID(s):
2240075
PAR ID:
10497542
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
ACM Transactions on Human-Robot Interaction
Date Published:
Journal Name:
ACM Transactions on Human-Robot Interaction
Volume:
13
Issue:
1
ISSN:
2573-9522
Page Range / eLocation ID:
1 to 17
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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