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Our research focuses on the fundamentals of learning, measuring, and understanding human-player experience and interaction in games. We conduct scientific research and build tools that provide information artifacts for use by advanced intelligent agents (bots), improve the game design & development process, and analyze the player performance and interactions to profile, model, and compare. Research in understanding human behavior in games helps us to explore and develop human-consistent, advanced interactive artificial intelligence that deepens the user immersion and improves interaction. We also explore ways to simplify the AI agent creation process for 3D games to bring new and creative opportunities to the masses. Our work truly incorporates all areas of computer science with an emphasis on Artificial Intelligence, Machine Learning, Knowledge Discovery (Data-mining), and Human Computer Interaction. Our research products include the Common Games Understanding & Learning (CGUL) Toolkit, the Urban Combat Testbed (UCT), and Dynamic Adaptive Super-Scalable Intelligent Entities (DASSIEs: BehaviorShop, BEHAVEngine, and FI3RST).

Our key research focus areas:

• Smart Simulation Objects
  Embedding intelligence and knowledge into objects, structures, and environmental elements within interactive virtual worlds.
• Advanced Simulation Agent Control and Creation
  Advancing the state-of-the-art in behavior-based control (a subsumption architecture extension) and exploring the next generation of Braitenberg inspired machines.
• Interactive Virtual World Data Analysis
  Investigating a deeper understanding of data streams from human and artificial agent interactions in game and simulation environments from single-player to massively multiplayer online (MMO).