Improvisational artificial intelligence for embodied co-creativity

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Jacob, Mikhail
Magerko, Brian
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This dissertation explores embodied agents that can improvise with people in an object-based gestural proto-narrative domain. I study the improvisational action selection problem (the challenge of performing action selection in an open-ended, ill-defined problem space in near real-time based on the agent’s knowledge and the improvisational context, in order to avoid incoherent behavior, decision paralysis, and unexpressive responses) and how to address it within the Robot Improv Circus interactive virtual reality (VR) installation and the CARNIVAL agent architecture. The CARNIVAL architecture uses affordance-based action variant generation, improvisational response strategies, and computational evaluation of creativity of perceived or generated actions to perform creative arc negotiation to address the improvisational action selection problem. Creative arc negotiation is the process of selecting actions over time to follow a given creative arc, i.e. a continuous target trajectory for generated responses through an agent’s creative space (consisting of novelty, surprise, and value). \My thesis statement states that “embodied agents that address the improvisational action selection problem using ‘creative arc negotiation’ increase perceptions of enjoyment, agent creativity, and coherence in both observers and participants while performing movement improv with non-experts.” My research found that it is valid to conclusively state that embodied agents addressing the improvisational action selection problem using creative arc negotiation can perform movement improv with non-experts so that perceptions of agent creativity and coherence increase for both VR participants and audience members. However, perceptions of enjoyment only conclusively increase for observers. More study is required to show a conclusive increase in enjoyment for VR participants of the installation.
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