<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper presents the concept of a control unit, i.e., a scenario player, for interactive training pilots in flight simulators. This scenario player is modelled as a hierarchy of finite state machines. Such an approach makes it possible to separate the details of an augmented reality display device which is used in training, from the core module of the system, responsible for contextual organization of the content. Therefore, the first contribution of this paper is the mathematical model of the scenario player as a universal formulation of the self-trained control unit for interactive learning systems, which is applicable in a variety of situations not limited solely to flight simulator related procedures. The second contribution is an experimental verification achieved by extensive simulations of the model, which proves that the proposed approach is capable to properly self-organize details of the context information by tracing preferences of the end users. For that latter purpose, the original algorithm is derived from statistical analysis, including Bayesian inference. The whole approach is illustrated by a real application of training the preflight procedure for the captain of the Boeing 737 aircraft in a flight simulator.</p>
</abstract>

The paper presents the concept of a control unit, i.e., a scenario player, for interactive training pilots in flight simulators. This scenario player is modelled as a hierarchy of finite state machines. Such an approach makes it possible to separate the details of an augmented reality display device which is used in training, from the core module of the system, responsible for contextual organization of the content. Therefore, the first contribution of this paper is the mathematical model of the scenario player as a universal formulation of the self-trained control unit for interactive learning systems, which is applicable in a variety of situations not limited solely to flight simulator related procedures. The second contribution is an experimental verification achieved by extensive simulations of the model, which proves that the proposed approach is capable to properly self-organize details of the context information by tracing preferences of the end users. For that latter purpose, the original algorithm is derived from statistical analysis, including Bayesian inference. The whole approach is illustrated by a real application of training the preflight procedure for the captain of the Boeing 737 aircraft in a flight simulator.

A hierarchy of finite state machines as a scenario player in interactive training of pilots in flight simulators

Department of Graphics, Computer Vision and Digital Systems

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The paper presents the concept of a control unit, i.e., a scenario player, for interactive training pilots in flight simulators. This scenario player is...

A hierarchy of finite state machines as a scenario player in interactive training of pilots in flight simulators

Published Online: Dec 30, 2021

Page range: 713 - 727

Received: Dec 14, 2020

Accepted: Aug 10, 2021

DOI: https://doi.org/10.34768/amcs-2021-0049

Keywords
Bayesian inference, deterministic Moore machine, flight simulator, hierarchy of finite state machines, scenario player

© 2021 Małgorzata Bach et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A hierarchy of finite state machines as a scenario player in interactive training of pilots in flight simulators

Published Online: Dec 30, 2021

Page range: 713 - 727

Received: Dec 14, 2020

Accepted: Aug 10, 2021

DOI: https://doi.org/10.34768/amcs-2021-0049

KeywordsBayesian inference, deterministic Moore machine, flight simulator, hierarchy of finite state machines, scenario player

© 2021 Małgorzata Bach et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Bayesian inference, deterministic Moore machine, flight simulator, hierarchy of finite state machines, scenario player