Curriculum Vitae

Jacob Schrum, Ph.D.

Associate Professor of Computer Science
Department of Mathematics and Computer Science
Southwestern University
1001 E. University Ave.
Georgetown, TX 78626

Office/Mail: Fondren-Jones Science Hall 308
Off-Campus Phone: (512) 863-1712
On-Campus Extension: x1712
E-mail: schrum2@southwestern.edu

I'm an Associate Professor of Computer Science at Southwestern University. I've been a member of the Department of Mathematics and Computer Science since 2014. Incidentally, Southwestern University is also where I received my undergraduate B.S. with a triple-major in Computer Science, Math, and German. I received my Masters and Ph.D. from the Computer Science Department at the University of Texas at Austin for my dissertation on Evolving Multimodal Behavior Through Modular Multiobjective Neuroevolution.

Classes Taught and Online Instructional Videos

Instructional videos associated with my classes are hosted on this YouTube channel.

Research Movies and Images

Individual videos associated with my research are linked to below, but are all hosted on this YouTube channel. More details about my research are available in my publications, also linked to below:

Research

My research area is Artificial Intelligence, specifically the automatic discovery of intelligent agent behavior and novel content, particularly in the domain of games. I'm interested in all sorts of games: board games, logic puzzles, and video games. Agents in video games and robot agents in the real world often require multiple modes of behavior (multimodal behavior) in order to handle multiple tasks. One powerful technique for discovering intelligent agent behavior is neuroevolution, which is the simulated evolution of artificial neural network brains. The complex domains I am interested in often involve multiple objectives, sometimes because separate tasks have separate objectives, so I am also interested in multiobjective optimization. Sometimes, focusing on multiple objectives is not enough, and a diverse range of agents and/or artefacts are needed, which I address using Quality Diversity algorithms. Domains requiring larger, more complex brains have sparked an interest in indirect encodings and deep learning. I also use these methods for Procedural Content Generation via Machine Learning in video game domains.

My dissertation advisor was Risto Miikkulainen of the Neural Networks Research Group (NNRG). Information about my research activities at the University of Texas is available on my Personal Page within the larger NNRG website.

My research has led to the development of several software packages:
  • Modular Multiobjective (Hyper) NEAT (MM-NEAT) is a software framework I developed in Java for evolving multimodal behavior in Ms. Pac-Man. It is an extension of the popular Neuro-Evolution of Augmenting Topologies algorithm. Work on MM-NEAT is ongoing, because I have undergraduate research students adding features to it every summer as part of the SCOPE undergraduate research experience. In fact, this repository is now much more than a mere extension of NEAT, but a collection of various tools that can be used for evolution and machine learning in various domains. You can track the most recent updates on GitHub.
  • A lot of work in MM-NEAT has focused on Procedural Content Generation for Games using Generative Adversarial Networks. This subset of MM-NEAT code is available in the GameGAN repository. It showcases work in Mario, Zelda, Mega Man, and Lode Runner, and is based on contributions by former SCOPE students Benjaman Capps, Jake Gutierrez, and Kirby Steckel, as well as work by external collaborators from around the world.
  • Another project derived from MM-NEAT is Quantum Zentanglement by undergraduate students Sarah Friday and Anna Krolikowski. The system evolves images, and then combines them in a way inspired by Zentangle art. Many examples are available here.
  • The Zentangle repo was actually a spin-off of a more general art platform for CPPN-based Art Evolution. This system was made by undergraduate students Lauren Gillespie and Isabel Tweraser. At its core, it is a reimplementation of the now defunct Picbreeder platform for evolving images with Compositional Pattern Producing Networks. However, it supports many more types of interactive evolution: voxel-based 3D shapes (like the now defunct Endless Forms site), soundwaves (similar to Breedesizer), as well as 2D and 3D animations, which was an original contribution by Isabel and Lauren associated with their SCOPE research.
  • The EvoCraft SCOPE repo allows for interactive evolution of interesting and novel shapes in Minecraft using the EvoCraft API. Work in Minecraft has also been conducted in MM-NEAT to evolve flying machines using Quality Diversity. This SCOPE research was conducted by Alejandro Medina, Melanie Richey, and Mark Mueller. Movies and other information related to research in Minecraft are available here.
  • UT^2 is a software agent for Unreal Tournament 2004 that won the 2012 BotPrize competition, a Turing Test for video game bots. The agent depends on the Pogamut platform, which is Java middleware that interfaces with Unreal Tournament 2004 via the included GameBots mod. Information about my past BotPrize research is compiled on this page. Note that this code and other code associated with Unreal Tournament 2004 is now included in MM-NEAT as well, and additional research in Unreal Tournament 2004 was conducted by SCOPE student Adina Friedman.
  • The Infinite Art Gallery is a video game in which players interactively evolve art similar to that of Picbreeder and Endless Forms, but by interacting with the art in an immersive 3D world. The code for this project is available on GitHub. It was developed in C# and Unity by undergraduate student Bryan Hollingsworth as part of SCOPE.
  • Multi-Brain HyperNEAT is an extension of HyperNEAT, an approach for evolving indirectly encoded neural networks. Multi-Brain HyperNEAT allows individual agents to have multiple separate brains to use in different circumstances. The code is an extension of the Multiagent Simulator for HyperSharpNEAT, which is a C# implementation of HyperNEAT available on this page.
  • BREVE Monsters is a 3D Artificial Life environment with several domains in which I evolved multimodal behavior. This code relies on the Breve simulation environment, which has unfortunately been discontinued.
I have also become increasingly involved in undergraduate research during my time at Southwestern University, thanks in part to the SCOPE program, which is a Summer research program for undergraduates at Southwestern University.

Dissertation


Peer-Reviewed Journal Articles


Peer-Reviewed Conference Publications


Invited Book Chapters/Articles


Extended Abstracts


Technical Reports


Dagstuhl Reports


Undergraduate Poster Presentations Supervised


Miscellaneous


Last Updated: 6/20/2024