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Simulation-based training (SBT) is gaining popularity as a low-cost andconvenient training technique in a vast range of applications. However, for aSBT platform to be fully utilized as an effective training tool, it isessential that feedback on performance is provided automatically in real-timeduring training. It is the aim of this paper to develop an efficient andeffective feedback generation method for the provision of real-time feedback inSBT. Existing methods either have low effectiveness in improving novice skillsor suffer from low efficiency, resulting in their inability to be used inreal-time. In this paper, we propose a neural network based method to generatefeedback using the adversarial technique. The proposed method utilizes abounded adversarial update to minimize a L1 regularized loss viaback-propagation. We empirically show that the proposed method can be used togenerate simple, yet effective feedback. Also, it was observed to have higheffectiveness and efficiency when compared to existing methods, thus making ita promising option for real-time feedback generation in SBT.

Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training