Improbotics: Exploring the Imitation Game Using Machine Intelligence in Improvised Theatre
Open accessAbstract
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. 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. All lines are provided to actors with headphones and all performers are wearing headphones. This paper describes a Turing test, or imitation game, taking place in a theatre, with both the audience members and the performers left to guess who is a human and who is a machine. In order to test scientific hypotheses about the perception of humans versus machines we collect anonymous feedback from volunteer performers and audience members. Our results suggest that rehearsal increases proficiency and possibility to control events in the performance. That said, consistency with real world experience is limited by the interface and the mechanisms used to perform the show. We also show that human-generated lines are shorter, more positive, and have less difficult words with more grammar and spelling mistakes than the artificial improvisor generated lines.
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The current state-of-the-art method for longer text generation incorporates a human-in-the-loop who selects from various generations sampled from the model.
“Because these models do not achieve long-term semantic coherence, the current state-of-the-art method for longer text generation incorporates a human-in-the-loop who selects from various generations sampled from the model [10,67,75].”
From Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals (2023) · cites this paper as [67]
Section 2.2 describes a human controller choosing among model-generated dialogue suggestions, and Section 4.3 says the resulting lines can still be narratively inconsistent. The paper does not establish that this is the state-of-the-art method for longer text generation; Section 4.5 describes its dialogue system as only one component of a larger story-generation system. Page numbers were not available in our copy. (LLM, conf=0.88)
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“Examples of such creative uses of LLMs include the generation of the script of short film Sunspring (2016) [76], It’s No Game (2017) or Sollicitors (2020) [1], improvisational theatre alongside robots by company Improbotics (2016) [10, 67, 69, 70], collaborative script writing for theatre play AI [109], and THEaiTRE c…”
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Self-citation
- Authors
- Mathewson, K., & Mirowski, P.
- Affiliations
- University of Alberta (CA)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
- Published
- 2018-09-25
- Volume
- 14
- Issue
- 1
- Pages
- 59-66
- ISSN
- 2326-909X (ISSN-L)
- 2334-0924
- OpenAlex
- W2890043461
- Cited by
- 18
- References
- 42
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 6 · 2023: 3 · 2021: 2 · 2020: 1 · 2019: 2
- FWCI
- 2.24
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- improvisational theatre, chatbots, Turing test, dialogue generation, deep learning, film subtitles, rehearsal, audience perception
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