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  <title><![CDATA[PhD Defense by Zhiyu Lin]]></title>
  <body><![CDATA[<p>&nbsp;</p>

<p>Zhiyu Lin</p>

<p>PhD Student in Computer Science</p>

<p>School of Interactive Computing, College of Computing</p>

<p>Georgia Institute of Technology</p>

<p>&nbsp;</p>

<p>Committee</p>

<p>Dr. Mark Riedl (Advisor, School of Interactive Computing, Georgia Institute of Technology)</p>

<p>Dr. Alan Ritter (School of Interactive Computing, Georgia Institute of Technology)</p>

<p>Dr. Gil Weinburg (School of Music, Georgia Institute of Technology)</p>

<p>Dr. Matthew Guzdial (Department of Computing Science, University of Alberta)</p>

<p>Dr. Wei Xu (School of Interactive Computing, Georgia Institute of Technology)</p>

<p>&nbsp;</p>

<p>Abstract</p>

<p>Recent advancements in controlling generative systems allowed more fine-grained control allowing automation of more parts of game development and creative content creation, several problems arise preventing human creators from fully utilizing these techniques, including challenges in high-level intent-based control, lack of intuitiveness, difficulty in personalization, AI-centric interfaces for human creators, and ultimately not aiming at enhancing the creativity of human designers in a collaborative manner.</p>

<p>To solve these problems, I purpose Human-Aware Artificial Intelligence Procedural Content Generation (HAAI-PCG), a mixed-initiative co-creativity system with human awareness, to overcome these problems.</p>

<p>I posit that such a system will empower high-level intent-based customization of contents and use the capability of high-level control to enable natural personalized collaborative interaction between a human designer and AI, to better inspire human designers to create content satisfied to both humans and AI than current PCG systems.</p>

<p>I have previously discovered how a PCG-Machine-Learning (PCGML) system can utilize high-level intents and how an AI agent take advice from human to learn faster and perform better;</p>

<p>I propose to study how a combination of these techniques will fuel a co-creativity system that builds the interactions between human designers and AI agents, by investigating (1) preferred interactions between participants of the system, (2) quality of co-created contents from this system, and (3) how the system will be perceived by human designers, the user of this system.</p>

<p>The investigation will open the door to the key understanding of major features in a human-aware co-creativity system, how human designers can benefit from it, and ultimately, the potential of co-creative systems in this new generation of AI and ML.</p>

<p>&nbsp;</p>
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