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Theatrical improvisation (impro or improv) is a demanding form of live,collaborative performance. Improv is a humorous and playful artform built on anopen-ended narrative structure which simultaneously celebrates effort andfailure. It is thus an ideal test bed for the development and deployment ofinteractive artificial intelligence (AI)-based conversational agents, orartificial improvisors. This case study introduces an improv show experimentfeaturing human actors and artificial improvisors. We have previously developeda deep-learning-based artificial improvisor, trained on movie subtitles, thatcan generate plausible, context-based, lines of dialogue suitable for theatre(Mathewson and Mirowski 2017). In this work, we have employed it to controlwhat a subset of human actors say during an improv performance. We also givehuman-generated lines to a different subset of performers. All lines areprovided to actors with headphones and all performers are wearing headphones.This paper describes a Turing test, or imitation game, taking place in atheatre, with both the audience members and the performers left to guess who isa human and who is a machine. In order to test scientific hypotheses about theperception of humans versus machines we collect anonymous feedback fromvolunteer performers and audience members. Our results suggest that rehearsalincreases proficiency and possibility to control events in the performance.That said, consistency with real world experience is limited by the interfaceand the mechanisms used to perform the show. We also show that human-generatedlines are shorter, more positive, and have less difficult words with moregrammar and spelling mistakes than the artificial improvisor generated lines.

Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre