Title: Human-Aware Artificial Intelligence Procedural Content Generation

Zhiyu Lin

Ph.D. Candidate in IC

School of Interactive Computing 

Georgia Institute of Technology  

Date: March 28, 2024

Time:  3 PM - 5:30 PM

Location:  CODA C1115 Druid Hills

Virtual link:https://gatech.zoom.us/j/92229176713?pwd=SGNCU1M5ektGN0l1MmtQNU1CMDRRZz09

Committee

Dr. Mark Riedl (Advisor, School of Interactive Computing, Georgia Institute of Technology)

Dr. Alan Ritter (School of Interactive Computing, Georgia Institute of Technology)

Dr. Gil Weinburg (School of Music, Georgia Institute of Technology)

Dr. Matthew Guzdial (Department of Computing Science, University of Alberta)

Dr. Wei Xu (School of Interactive Computing, Georgia Institute of Technology)

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 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 further 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 awareness of 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 systems, and the benefits of MI-CC systems covering larger portions of the design space. In light of these findings, I demonstrate the potential of Reinforcement Learning (RL) with human-in-the-loop, and further showcase how this method enables human-awareness of MI-CC collaborative systems beyond controlled generation, learning collaborative delegations and improving overall experiences.

Zoom Meeting Details

Topic: Zhiyu's Dissertation Defense

Time: Mar 28, 2024 03:00 PM Eastern Time (US and Canada)

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