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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jaeseok Huh & Jonghun Park & Dongmin Shin & Yerim Choi, 2019. "A Hierarchical SVM Based Behavior Inference of Human Operators Using a Hybrid Sequence Kernel," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:4836-:d:264120
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