错误树分析 - 适应知识结构:可持续国际安全合作的案例研究
Yudai Wada1, Koki Ijuin1, Chiaki Oshiyama1
1School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Japan.
Frontiers in artificial intelligence
|February 23, 2026
概括
本研究介绍了一种知识工程方法,通过将专业知识外化为机器可解读图表来改善日本在国际安全合作中可持续的知识转移. 这种方法提高了连续性,并减少了人员更换期间的知识损失.
科学领域:
- 知识管理知识管理
- 国际安全合作 国际安全合作
- 系统工程 系统工程
背景情况:
- 在日本的国际安全合作中,可持续的知识转移面临着由于制度约束,默认知识和人员轮换的挑战.
- 传统的培训方法不足以在研发和采购环境中实现持久的知识转移.
- 知识丢失是一个重大问题,影响了项目的连续性,并可能导致重复的错误.
研究的目的:
- 提出和评估一种新的知识工程方法,用于将安全合作中从业人员的专业知识外部化.
- 开发一个系统,以程序为基础的和基于目的的知识结构化为可审计,机器可解释的图表.
- 加强知识连续性,减轻国际安全研发和采购方面的知识损失.
主要方法:
- 开发了一种知识工程方法,使用令人信服的人类行动合理化模型 (CHARM) 进行知识结构.
- 综合断层树分析 (FTA) 来引出和阐明隐含的因果推理和目的-动作对.
- 创建可审计,链接,机器可解释的程序和基于目的的知识图,嵌入参考案例 (RCs) 与FTA衍生的注释.
- 通过面试和研讨会的定性和定量分析,经验评估了该方法的有效性.
主要成果:
- 该方法成功地将从业人员的专业知识外化,产生了42个参考案例 (RC) 和133个与案例相关的行动.
- 外化知识显示增加了体积和细节性,形成了具有来源的目的行动单元.
- 这种方法产生了可重复使用,机器可解释的资产,适合人类-人工智能协作.
- 根据FTA调整的CHARM方法显示了在移交和谈判准备期间改善连续性的潜力.
结论:
- 拟议的知识工程方法,集成FTA适应的CHARM,有效地解决了日本国际安全合作可持续知识转移的挑战.
- 创建机器可解读的知识图可以促进知识的连续性,并减少对默认知识的依赖.
- 这种方法提供了一种可行的解决方案,用于减轻知识丢失,提高复杂的国际合作中的运营效率.
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