2023 ASEE Annual Conference & Exposition

Educating the next generation of AI researchers requires methods which teach the software tools, theoretical concepts, and domain knowledge specific to the field. To help develop these key skills, we focus particularly on the area of Swarm AI, which, in general, covers the autonomous operation of a large number of agents in a single environment. Applications of this field are numerous, including autonomous navigation, defense, robotics, logistics, and search/rescue. Despite the potential for impact in key problem domains and interdisciplinary nature, Swarm AI platforms are note generally used to develop competency in AI education. While some courses and tracks target AI in general, there are few tools to help engage learners in the specific area of Swarm AI. This is partly due to needing strong skills in the intersection of reinforcement learning, software development, and robotics, as well as a framework computing capability on which to test and evaluate. We propose Battle Optimized Laser Tag (BOLT), a Unity-built simulator that enables learners to develop Swarm AI algorithms in the context of a laser tag game between small mobile robots. The simulator is built to be familiar to a video game environment to maximize engagement, while the included documentation for the environment is designed as a gentle introduction into the topic of controlling swarms. Easily modified configurations enable educators to specify tasks and develop curricula to further challenge learners. Furthermore, the simulator is OS-agnostic, simple to install and uses Python to interface with the agents. This allows learners to implement their solutions on their own computers in a programming language common to other AI courses. Finally, extensions of the platform, leveraging the Python interface, can be deployed to physical robots or be used as a testbed for other AI domains beyond robotics including Human-Machine Teaming and Cybersecurity.