Title:
Human-Aware Artificial Intelligence Procedural Content Generation

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Author(s)
Lin, Zhiyu
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Advisor(s)
Riedl, Mark O.
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School of Computer Science
School established in 2007
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Abstract
Although recent advancements in Machine Learning (ML)-based Artificial Intelligence (AI) generative models enabled a new generation of Computational Creative capabilities unimaginable before, many of them are AI-centric, barring many human creators without an in-depth understanding of these AI models from building effective communications between them and the systems, and utilizing both the expertise of their own and the AI models. My research focuses on Human-Aware Artificial Intelligence Procedural Content Generation (PCG), which centers on empowering creator-aware ways to carry out Procedural Content Generation tasks, enabling more creator-aware information exchange between a human creator and the AI, and abilities for the AI agent to adapt to the specific human creator while collaborating on the fly. In this dissertation, I begin with a discussion of what Computational Creativity means to the human-AI collaborative partnership by illustrating the diversity of Co-creative systems and sketching out the fundamentals of my work. I then present case studies of AI PCG systems utilizing both high-level and fine-grained control knobs with an awareness of the human creative process in mind. Developing on these studies, I cast the spotlight onto Creative-Wand, the toolbox I developed to explore the design space of interactions for Mixed-Initiative Co-Creative (MI-CC) systems, and the benefits of MI-CC systems covering larger portions of the design space. In light of these findings, I demonstrate that human-in-the-loop Reinforcement Learning (RL) can enable human awareness of MI-CC collaborative systems, going beyond controlled generation, learning collaborative delegations, and improving overall experiences.
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Date Issued
2024-04-25
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Dissertation
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