Title: Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training,
Abstract: Simulation-based training (SBT) is gaining popularity as a low-cost and
convenient training technique in a vast range of applications. However, for a
SBT platform to be fully utilized as an effective training tool, it is
essential that feedback on performance is provided automatically in real-time
during training. It is the aim of this paper to develop an efficient and
effective feedback generation method for the provision of real-time feedback in
SBT. Existing methods either have low effectiveness in improving novice skills
or suffer from low efficiency, resulting in their inability to be used in
real-time. In this paper, we propose a neural network based method to generate
feedback using the adversarial technique. The proposed method utilizes a
bounded adversarial update to minimize a L1 regularized loss via
back-propagation. We empirically show that the proposed method can be used to
generate simple, yet effective feedback. Also, it was observed to have high
effectiveness and efficiency when compared to existing methods, thus making it
a promising option for real-time feedback generation in SBT.