RA, Fifth year PhD Student
Email:    Sps74[at]Drexel[dot]edu

Working under my advisor, Dr. Santiago Ontañón Villar, I have been pursuing research focused on Artificial Intelligence (AI).  Some topics in this area that interest me are Procedural Content Generation (PCG) and machine learning.  In particular, I am interested in how those areas could be applied to the domain of games. An online demo of a portion of my level generation project can be found here.

I received my B.S. in Computer Science from Ursinus College in 2012.



Publications

Sam Snodgrass and Santiago Ontanón. "Player Movement Models for Video Game Level Generation." Twenty-Sixth International Joint Conference on Artificial Intelligence. 2017. (accepted) [PDF to come]

Summerville, Adam, Sam Snodgrass, Matthew Guzdial, Christoffer Holmgård, Amy K Hoover, Aaron Isaksen, Andy Nealen, and Julian Togelius. "Procedural Content Generation via Machine Learning (PCGML)." arXiv preprint arXiv:1702.00539. 2017. [PDF]

Snodgrass, Sam, and Santiago Ontanón. "Learning to Generate Video Game Maps Using Markov Models." IEEE Transactions on Computational Intelligence and AI in Games. 2016. [PDF]

Snodgrass, Sam, and Santiago Ontanón. "An Approach to Domain Transfer in Procedural Content Generation of Two-Dimensional Videogame Levels." Twelfth Artificial Intelligence and Interactive Digital Entertainment Conference. 2016. [PDF]

Summerville, Adam James, Sam Snodgrass, Santiago Ontanón, and Michael Mateas. "The VGLC: The Video Game Level Corpus." Proceedings of the 7th Workshop on Procedural Content Generation at 1st Joint International Conference of DiGRA and FDG. 2016. [PDF]

Snodgrass, Sam, and Santiago Ontanón. "Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling." Twenty-Fifth International Joint Conference on Artificial Intelligence. 2016. [PDF]

Snodgrass, Sam, and Santiago Ontañón. "A Hierarchical MdMC Approach to 2D Video Game Map Generation." Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference. 2015. [PDF]

Snodgrass, Sam, and Santiago Ontañón. "A Hierarchical Approach to Generating Maps Using Markov Chains." Tenth Artificial Intelligence and Interactive Digital Entertainment Conference. 2014. [PDF]

Snodgrass, Sam, Benjamin Goldberg, Ariel Evans, Brandon Packard, Cathy Lu, and Jichen Zhu. "Extended abstract for Canvas Obscura." Proceedings of the first ACM SIGCHI annual symposium on Computer-human interaction in play. ACM, 2014. [PDF]

Snodgrass, Sam, and David W. Aha. "System Model Formulation Using Markov Chains." (Technical Note AIC-14-170). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in AI. 2014.[Technical Report]

Ontañón, Santiago, Yi-Ching Li, Sam Snodgrass, Dana Bonfiglio, Flaura K. Winston, Catherine McDonald, and Avelino J. Gonzalez. "Case-Based Prediction of Teen Driver Behavior and Skill." Case-Based Reasoning Research and Development. Springer International Publishing, 2014. 375-389. [PDF]

Snodgrass, Sam, and Santiago Ontañón. "Experiments in Map Generation using Markov Chains." Ninth International Conference on the Foundations of Digital Games. 2014. [PDF]

Snodgrass, Sam, and Santiago Ontañón. "Generating Maps Using Markov Chains." Ninth Artificial Intelligence and Interactive Digital Entertainment Conference. 2013. [PDF]

Projects
  • Exploring procedural content generation via machine learning (PCGML)
    • Multi-dimensional Markov chains (MdMCs)
    • Hierarchical multi-dimensional Markov chains (HMdMCs)
    • Markov Random Fields (MRFs)
    • Constrained MdMC Sampling
    • Domain Transfer between games
      • The .zip file below contains the code for the MdMC and HMdMC models as well as the training sets used
      • Source code (README included in .zip)
      • If you would like to use the source code for your own project, cite this paper: 
        • Snodgrass, Sam, and Santiago Ontañón. "A Hierarchical MdMC Approach to 2D Video Game Map Generation." Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference. 2015.
    • If you have any question or comments regarding the code or the data sets, feel free to contact me
    • Additional data sets can be found in the Video-game Level Corpus
      • If you use the VGLC, please cite this paper:
        • Summerville, Adam James, et al. "The VGLC: The video game level corpus." Proceedings of the 7th Workshop on Procedural Content Generation. 2016.
  • Driver behavior modeling
    • Predicting driver behavior
    • Classifying drivers based on their behavior
  • Canvas Obscura: A horror game with procedurally generated level layouts.
Teaching Assistantships at Drexel University

 Term  Course 
 Spring 2014  CS 380: Artificial Intelligence
 Winter 2014  CS 260: Data Structures
 Fall 2013
 CS 380: Artificial Intelligence
 Spring 2013  CS 123: Computation Lab 3
 Winter 2013  CS 122: Computation Lab 2
 Fall 2012  CS 121: Computation Lab 1

Links

Alberto Uriarte, PhD student at the Drexel AI and Games Lab
Josep Valls-VargasPhD student at the Drexel AI and Games Lab