I recently completed my PhD in Computer Science at UNC Charlotte. I worked in the Games Intelligence Group in the Games + Learning Lab at UNC Charlotte under the supervision of Michael Youngblood. My research focus is on developing a flexible, resource aware engine for game AI agents in support of a graphical user interface for building game agents more easily. I started working at Nokia in September 2011, and I am not looking for a job at this time.

I've also spent some time working on robotics middleware. Visit wurde.sf.net for more information on WURDE. Other work has included a framework for accelerating object detection with Michael Dixon.

PhD Dissertation:

Dynamic Behavior-Based Control and World-Embedded Knowledge for Interactive Artificial Intelligence.
Defended April 27, 2011, Accepted June 29, 2011.

Video game designers depend on artificial intelligence to drive player experience in modern games. Therefore it is critical that AI not only be fast and computationally inexpensive, but also easy to incorporate with the design process. We address the problem of building computationally inexpensive AI that eases the game design process and provides strategic and tactical behavior comparable with current industry-standard techniques.

Our central hypothesis is that behavior-based characters in games can exhibit effective strategy and coordinate in teams through the use of knowledge embedded in the world and a new dynamic approach to behavior-based control that enables characters to transfer behavioral knowledge. We use dynamic extensions for behavior-based subsumption and world-embedded knowledge to simplify and enhance game character intelligence. We find that the use of extended affordances to embed knowledge in the world can greatly reduce the effort required to build characters and AI engines while increasing the effectiveness of the behavior controllers. In addition, we find that the technique of multi-character affordances can provide a simple mechanism for enabling team coordination. We also show that reactive teaming, enabled by dynamic extensions to the subsumption architecture, is effective in creating large adaptable teams of characters. Finally, we show that the command policy for reactive teaming can be used to improve performance of reactive teams for tactical situations.



UNC Charlotte

Washington University in St. Louis

Swarthmore College