Using Artificial Intelligence in Augmented Environments for Complex Training Scenarios in the Defense Industry
Using Artificial Intelligence in Augmented Environments for Complex Training Scenarios in the Defense Industry

Summary/Abstract: Classical training scenarios in exercises and practices in the defense industry, focus on realism, where complex scenarios are simulated to achieve maximum impact on unit training. Thousands of hours are being spent to train units and provide real-life scenarios, to improve on survivability and efficiency of units, if and when the time comes, for proven organizational and practical efficiency. Using Augmented Reality and Artificial Intelligence to create real-life simulated environments would create a totally new learning platform, which provides a new learning experience and approach, for specialized situations in the defense industry. Many other solutions have tried to provide for interactive content, without achieving the much needed interaction between subjects participating in training courses, where complex environmental relations need to be analyzed. Artificial Intelligence algorithms may help identify patterns and may be trained to provide for specific input on human interaction. The Augmented Reality environment may provide for the much needed interaction and practices that are normally required for real-life situations. Combining both elements into a single eLearning platform, may provide for a totally new paradigm in the training of military and intelligence units, by reducing costs on an already expansive military budget, providing a safer training environment and ultimately improving the available methods of training for a special domain. The present work makes an analysis of available technology, software and hardware, to provide for such a scenario where Artificial Intelligence and Augmented Reality is united to create a new e-learning concept. A functional architecture will be presented, with the identification of the most probable already existing technologies that could be used to reach a proof-of-concept solution.

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