Self-Explaining Agents in Virtual Training

Virtual training systems are increasingly used for the training of complex, dynamic tasks. To give trainees the opportunity to train autonomously, intelligent agents are used to generate the behavior of the virtual players in the training scenario. For effective training however, trainees should be supported in the reflection phase of the training as well. Therefore, we propose to use self-explaining agents, which are able to generate and explain their own behavior. The explanations aim to give a trainee insight into other players’ perspectives, such as their perception of the world and the motivations for their actions, and thus facilitate learning. Our project investigates the possibilities of self-explaining agents in virtual training systems, and the effects on learning.