Abstract
Autonomous systems, although capable of performing complicated tasks much faster than humans, are brittle due to uncertainties encountered in most real-time applications. People supervising these systems often rely on information relayed by the system to make any decisions, which places a burden on the system to self-assess its proficiency and communicate the relevant information.
Proficiency self-assessment benefits from an understanding of how well the models and decision mechanisms used by robot align with the world and a problem holder’s goals. This paper makes three contributions: (1) Identifying the importance of goal, system, and environment for proficiency assessment; (2) Completing the phrase “proficient ‹preposition›” using an understanding of proficiency span; and (3) Proposing the proficiency dependency graph to represent causal relationships that contribute to failures, which highlights how one can reason about their own proficiency given alterations in goal, system, and environment.
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Acknowledgments
This work was supported in part by the U.S. Office of Naval Research under Grants N00014-18-1-2503 and N00014-16-1-302. All opinions, findings, conclusions, and recommendations expressed in this paper are those of the author and do not necessarily reflect the views of the Office of Naval Research.
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Gautam, A., Crandall, J.W., Goodrich, M.A. (2021). Self-assessment of Proficiency of Intelligent Systems: Challenges and Opportunities. In: Zallio, M. (eds) Advances in Human Factors in Robots, Drones and Unmanned Systems. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1210. Springer, Cham. https://doi.org/10.1007/978-3-030-51758-8_15
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DOI: https://doi.org/10.1007/978-3-030-51758-8_15
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