Shared autonomy for an interactive AI system

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 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 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.