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Title: Adaptive CGF for pilots training in air combat simulation
Authors: Teng, Teck-Hou
Tan, Ah-Hwee
Ong, Wee-Sze
Lee, Kien-Lip
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Teng, T.-H., Tan, A.-H., Ong, W-S., & Lee, K.-L. (2012). Adaptive CGF for pilots training in air combat simulation. 2012 15th International Conference on Information Fusion (FUSION), 2263 - 2270.
Abstract: Training of combat fighter pilots is often conducted using either human opponents or non-adaptive computer-generated force (CGF) inserted with the doctrine for conducting air combat mission. The novelty and challenges of such non-adaptive doctrine-driven CGF is often lost quickly. Incorporating more complex knowledge manually is known to be tedious and time-consuming. Therefore, a study of using adaptive CGF to learn from the real-time interactions with human pilots to extend the existing doctrine is conducted in this work. The goal of this study is to show how an adaptive CGF can be more effective than a non-adaptive doctrine-driven CGF for simulator-based training of combat pilots. Driven by a family of self-organizing neural network, the adaptive CGF can be inserted with the same doctrine as the non-adaptive CGF. Using a commercial-grade training simulation platform, two human-in-the-loop (HIL) experiments are conducted using the adaptive CGF and the non-adaptive doctrine-driven CGF to engage two diverse groups of human pilots in 1-v-1 dogfights. The quantitative results and qualitative assessments of the CGFs by the human pilots are collected for all the training sessions. The qualitative assessments show the trainee pilots are able to match the adaptive CGF to the desirable attributes while the veteran pilots are only able to observe some learning from the adaptive CGF. The quantitative results show that the adaptive agent needs a lot more training sessions to learn the necessary knowledge to match up to the human pilots.
Rights: © 2012 International Society of Information Fusion. This paper was published in 2012 15th International Conference on Information Fusion (FUSION) and is made available as an electronic reprint (preprint) with permission of International Society of Information Fusion. The paper can be found at the following official URL: One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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