Learning to Optimize Autonomy in Competence-Aware Systems
- URL: http://arxiv.org/abs/2003.07745v1
- Date: Tue, 17 Mar 2020 14:31:45 GMT
- Title: Learning to Optimize Autonomy in Competence-Aware Systems
- Authors: Connor Basich, Justin Svegliato, Kyle Hollins Wray, Stefan Witwicki,
Joydeep Biswas, Shlomo Zilberstein
- Abstract summary: We propose an introspective model of autonomy that is learned and updated online through experience.
We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback.
We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains.
- Score: 32.3596917475882
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to
deploy autonomous systems in domains that require occasional reliance on
humans. This paradigm allows service robots or autonomous vehicles to operate
at varying levels of autonomy and offer safety in situations that require human
judgment. We propose an introspective model of autonomy that is learned and
updated online through experience and dictates the extent to which the agent
can act autonomously in any given situation. We define a competence-aware
system (CAS) that explicitly models its own proficiency at different levels of
autonomy and the available human feedback. A CAS learns to adjust its level of
autonomy based on experience to maximize overall efficiency, factoring in the
cost of human assistance. We analyze the convergence properties of CAS and
provide experimental results for robot delivery and autonomous driving domains
that demonstrate the benefits of the approach.
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