Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre
Kory W. Mathewson, Piotr Mirowski

TL;DR
This paper explores the use of AI-generated dialogue in improvised theatre, conducting a Turing test with audience and performers to assess human versus machine perception and performance quality.
Contribution
It introduces a novel integration of AI improvisors in live theatre and evaluates perception and performance differences through a real-world experiment.
Findings
Rehearsal improves performance control and proficiency.
Audience and performers struggle to distinguish AI from humans.
AI lines are less positive, shorter, and contain more errors.
Abstract
Theatrical improvisation (impro or improv) is a demanding form of live, collaborative performance. Improv is a humorous and playful artform built on an open-ended narrative structure which simultaneously celebrates effort and failure. It is thus an ideal test bed for the development and deployment of interactive artificial intelligence (AI)-based conversational agents, or artificial improvisors. This case study introduces an improv show experiment featuring human actors and artificial improvisors. We have previously developed a deep-learning-based artificial improvisor, trained on movie subtitles, that can generate plausible, context-based, lines of dialogue suitable for theatre (Mathewson and Mirowski 2017). In this work, we have employed it to control what a subset of human actors say during an improv performance. We also give human-generated lines to a different subset of performers.…
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Taxonomy
TopicsReinforcement Learning in Robotics · Music Technology and Sound Studies · Neural Networks and Reservoir Computing
