Army Crew Training: Coaching with Intelligent Tutoring System (ITS)

Open Access
Article
Conference Proceedings
Authors: Vlad Zotov

Abstract: The training of military crews of armoured vehicles can be enhanced by applying AI-based methods to the training drills. Defence Research and Development Canada used a Human Behaviour Representation approach to create an armoured crew simulation trainer for the Canadian Armed Forces. The Human Behaviour Representation (HBR) approach is a form of rule-based AI that applies a cognitive task analysis to derive a synthetic operator. The cognitive task analysis resulted in a Task Network Model (TNM) for each crew member of the Light Armoured Vehicle (LAV) and for the entire crew. These TNMs were inputted into a discrete event simulator to create a synthetic training environment that combines virtual and human members of the LAV crew. The training platform allows a human member of the team to interact with the synthetic crew through voice production software that was integrated with the synthetic environment.The paper presents the development of the Intelligent Tutoring System module for the LAV crew simulation platform that serves as a human instructor for conducting basic LAV drills. The paper outlines the architecture, functionality, and testing of the module. The work shows how the HBR approach can be used to develop a synthetic coach for training a military crew. The work is a step in developing and testing a general training system for small military teams. The training system will allow to conduct basic crew drills, in which a human crew member will be trained with the synthetic crew members, thus overcoming some of the obstacles that military crew training faces: a logistic difficulty to gather a full crew at the same time and place and a deficiency of qualified instructors. The paper outlines the steps for the follow-up work required to develop a generic AI-based autonomous systems for basic training of small military teams.

Keywords: synthetic teammates, Human Behaviour Representation, Intelligent Tutoring System

DOI: 10.54941/ahfe1002694

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