In this collaboration with ororatech, the students worked on building CNN-based methods to predict future spread of wildfires. Besides the previous fire masks, multiple additional satellite data sources were integrated, providing, e.g., weather data, landcover types, and elevation information.
Reducing the time that an ambulance needs to arrive at an incidents can save lives. Dynamically redeploying ambulances to different basestations thus can save lives. The students worked on implementing a discrete event based simulation that replays real-world incidents, processed multiple incident datasets and implemented existing baselines. In order to redeploy ambulances predicting the future demand plays a vital roll. Therefore, students evaluated and implemented various ambulance demand prediction models.
Schafkopf is a traditional Bavarian card game that has complex interaction with other team members (ad-hoc teamplay). The students worked on implementing a high-performance c++ simulator and implemented various agents ranging from rule-based, monte-carlo tree search, to reinforcement learning based agents.