A Methodology for Constructing Intelligent-Machine FDC Commander Using Decision-Making Tree 


Vol. 45,  No. 2, pp. 355-363, Feb.  2020
10.7840/kics.2020.45.2.355


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  Abstract

This paper is to present a methodology to change a current system of battalion tactical fire command into an AI applied intelligent system. Implementing real AI connects into an effort to construct an AI platform. For this, civilian technologies should be cooperated with military domain. In the process of sensing., decision-making, and shooting where shooting should be done within 5~15 minutes once sensing is evoked, the meaning is enormous if at least 1~2 minutes are reduced by assisting inexperenced FDC commander through an intelligent system. The fact that the kind and the number of munitions are key elements in the FDC decision-making is identified. If JMEM DB construction is complete, ANN can be efficient, because the munitions effectiveness index can be used for ANN error modification. However because the DB construction is ongoing and it will be impossible to experiment all the size and the kind of targets which will be infinite, we apply decision-making tree algorithm which is not dependent on whether the JMEM DB construction is done. A framework for learning a decision tree and a methodology to implement the tree are proposed, applied to battle-field management systems.

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  Cite this article

[IEEE Style]

C. Han, "A Methodology for Constructing Intelligent-Machine FDC Commander Using Decision-Making Tree," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 2, pp. 355-363, 2020. DOI: 10.7840/kics.2020.45.2.355.

[ACM Style]

Changhee Han. 2020. A Methodology for Constructing Intelligent-Machine FDC Commander Using Decision-Making Tree. The Journal of Korean Institute of Communications and Information Sciences, 45, 2, (2020), 355-363. DOI: 10.7840/kics.2020.45.2.355.

[KICS Style]

Changhee Han, "A Methodology for Constructing Intelligent-Machine FDC Commander Using Decision-Making Tree," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 2, pp. 355-363, 2. 2020. (https://doi.org/10.7840/kics.2020.45.2.355)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880