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Recommendation Model for Battlefield Analysis based on Siamese Network

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(1), pp.1-8
  • DOI : 10.9708/jksci.2023.28.01.001
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 1, 2022
  • Accepted : December 16, 2022
  • Published : January 31, 2023

Geewon Suh 1 Yukyung Shin 2 Soyeon Jin 2 Woosin Lee 3 Jongchul Ahn 4 Changho Suh 1

1한국과학기술원
2한화시스템
3광운대학교
4한화 시스템

Accredited

ABSTRACT

In this paper, we propose a training method of a recommendation learning model that analyzes the battlefield situation and recommends a suitable hypothesis for the current situation. The proposed learning model uses the preference determined by comparing the two hypotheses as a label data to learn which hypothesis best analyzes the current battlefield situation. Our model is based on Siamese neural network architecture which uses the same weights on two different input vectors. The model takes two hypotheses as an input, and learns the priority between two hypotheses while sharing the same weights in the twin network. In addition, a score is given to each hypothesis through the proposed post-processing ranking algorithm, and hypotheses with a high score can be recommended to the commander in charge.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.