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- W2897539521 abstract "Across many domains, interactive systems either make decisions for us autonomously or yield decision-making authority to us and play a supporting role. However, many settings, such as those in education or the workplace, benefit from sharing this autonomy between the user and the system, and thus from a system that adapts to them over time. In this paper, we pursue two primary research questions: (1) How do we design interfaces to share autonomy between the user and the system? (2) How does shared autonomy alter a users perception of a system? We present SharedKeys, an interactive shared autonomy system for piano instruction that plays different video segments of a piece for students to emulate and practice. Underlying our approach to shared autonomy is a mixed-observability Markov decision process that estimates a users desired autonomy level based on her performance and attentiveness. Pilot studies revealed that students sharing autonomy with the system learned more quickly and perceived the system as more intelligent." @default.
- W2897539521 created "2018-10-26" @default.
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- W2897539521 date "2018-10-11" @default.
- W2897539521 modified "2023-10-14" @default.
- W2897539521 title "Shared Autonomy for an Interactive AI System" @default.
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- W2897539521 doi "https://doi.org/10.1145/3266037.3266088" @default.
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