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

TL;DR

This paper introduces a neural network-based adversarial approach to generate real-time feedback in simulation-based training, improving effectiveness and efficiency over existing methods.

Contribution

It presents a novel adversarial neural network method for real-time feedback generation in SBT, addressing limitations of prior approaches.

Findings

Effective feedback generation demonstrated in experiments

High efficiency and effectiveness compared to existing methods

Suitable for real-time application in training environments

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.