Learning Agents for Improved Efficiency and Effectiveness in Simulation-Based Training
||Learning Agents for Improved Efficiency and Effectiveness in Simulation-Based Training|
||SNIC Medium Compute|
||Fredrik Heintz <firstname.lastname@example.org>|
||2020-12-01 – 2021-12-01|
Team training in complex domains often requires a substantial amount of resources, e.g., instructors, role-players and vehicles. For this reason, it may be difficult to realize efficient and effective training scenarios in a real-world setting. Instead, intelligent agents can be used to construct synthetic, simulation-based training environments. However, building behavior models for such agents is challenging, especially for the end-users of the training systems, who typically do not have expertise in artificial intelligence. In this PhD project, we study how machine learning can be used to simplify the process of constructing agents for simulation-based training. By constructing smarter synthetic agents the dependency on human training providers can be reduced, and the availability as well as the quality of training can be improved.