Generally Genius: A Agent Development and Data Collection Framework


  • Aaditya Bhatia University of Central Florida
  • Austin Davis University of Central Florida
  • Soumik Ghosh University of Central Florida
  • Gita Sukthankar University of Central Florida



Agent Development, Real-time Strategy Game, Real-time Data Collection, Framework Design,, Telemetry, Imperfect Information, Microservices, Performance Analysis


We present an agent development and data collection framework for (GIO)--a real-time strategy game with imperfect information in which players attempt to gain control of opponents' starting positions within a 2D grid world. The framework provides event-based communication amongst several modules implemented as microservices, enabling real-time data collection from GIO's streaming data. Its modular design facilitates rapid bot development and testing, while the emphasis on data collection makes it easy to analyze agent performance. We use this framework in a case study of a top-performing GIO agent called Flobot. Our analysis demonstrates that Flobot's performance varies based on its starting position. Based on the analysis performed with our framework, we propose a modification to Flobot's pathfinding algorithm. Statistical tests show that the new algorithm results in a significant reduction in performance variance.




How to Cite

Bhatia, A., Davis, A., Ghosh, S., & Sukthankar, G. (2023). Generally Genius: A Agent Development and Data Collection Framework. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1), 400-406.