설명 가능한 인공지능(XAI)을 이용한 확률 임베딩 벡터 예측에 관한 연구

다국어 초록 (Multilingual Abstract)

eXplanable Artificial Intelligence(XAI) has recently gained attention as a technology that can enhance the performance and explanatory power of existing machine learning systems. In this study, we conducted research to quantitatively apply XAI methods to a defense policy known as Lessons Learned(LL) Analysis. Specifically, we propose a new LL analysis method that applies LDA topic modeling for AI NLP. The objective is not only to predict the information obtained from this method and analyze its basis, but also to assist non-expert AI users and combat field commanders in making quick judgments.