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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 user"s 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 user"s 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.

Shared Autonomy for an Interactive AI System