A Hierarchical SVM Based Behavior Inference of Human Operators Using a Hybrid Sequence Kernel

Jaeseok Huh, Jonghun Park, Dongmin Shin and Yerim Choi
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Jaeseok Huh: Department of Business Administration, Korea Polytechnic University, 237 Sangidaehak-ro, Siheung-si 15073, Korea
Jonghun Park: Department of Industrial Engineering and Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Dongmin Shin: Department of Industrial and Management Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Korea
Yerim Choi: Department of Industrial and Management Engineering, Kyonggi University, 154-42 Gwanggyosan-ro, Yeongtong-gu, Suwon-si 16227, Korea

Sustainability, 2019, vol. 11, issue 18, 1-16

Abstract: To train skilled unmanned combat aerial vehicle (UCAV) operators, it is important to establish a real-time training environment where an enemy appropriately responds to the action performed by a trainee. This can be addressed by constructing the inference method for the behavior of a UCAV operator from given simulation log data. Through this method, the virtual enemy is capable of performing actions that are highly likely to be made by an actual operator. To achieve this, we propose a hybrid sequence (HS) kernel-based hierarchical support vector machine (HSVM) for the behavior inference of a UCAV operator. Specifically, the HS kernel is designed to resolve the heterogeneity in simulation log data, and HSVM performs the behavior inference in a sequential manner considering the hierarchical structure of the behaviors of a UCAV operator. The effectiveness of the proposed method is demonstrated with the log data collected from the air-to-air combat simulator.

Keywords: behavior inference; hierarchical support vector machine; hybrid sequence kernel; human operator; unmanned combat aerial vehicle; simulation log data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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