Fault tree analysis-adapted knowledge structuring: a case study of sustainable international security cooperation.

Area of Science:

• Knowledge Management
• International Security Cooperation
• Systems Engineering

Background:

• Sustainable knowledge transfer in Japan's international security cooperation faces challenges due to institutional constraints, tacit knowledge, and personnel rotations.
• Traditional training methods are insufficient for durable knowledge transfer in R&D and procurement contexts.
• Knowledge loss is a significant issue, impacting project continuity and potentially leading to repeated errors.

Purpose of the Study:

• To propose and evaluate a novel knowledge engineering method for externalizing practitioner expertise in security cooperation.
• To develop a system for structuring procedure-based and purpose-based knowledge into auditable, machine-interpretable graphs.
• To enhance knowledge continuity and mitigate knowledge loss in international security R&D and procurement.

Main Methods:

• Developed a knowledge engineering method using the Convincing Human Action Rationalized Model (CHARM) for knowledge structuring.
• Integrated Fault Tree Analysis (FTA) to elicit and articulate implicit causal reasoning and purpose-action pairs.
• Created auditable, linked, machine-interpretable procedure- and purpose-based knowledge graphs, embedding Reference Cases (RCs) with FTA-derived annotations.
• Empirically assessed the method's effectiveness through qualitative and quantitative analysis of interviews and a workshop.

Main Results:

• The method successfully externalized practitioner expertise, yielding 42 Reference Cases (RCs) and 133 case-attached actions.
• Externalized knowledge demonstrated increased volume and granularity, forming provenance-bearing purpose-action units.
• The approach generated reusable, machine-interpretable assets suitable for human-AI collaboration.
• The FTA-adapted CHARM approach showed potential for improving continuity during handovers and negotiation preparedness.

Conclusions:

• The proposed knowledge engineering method, integrating FTA-adapted CHARM, effectively addresses challenges in sustainable knowledge transfer for Japan's international security cooperation.
• The creation of machine-interpretable knowledge graphs facilitates knowledge continuity and reduces reliance on tacit knowledge.
• This approach offers a viable solution for mitigating knowledge loss and improving operational effectiveness in complex international collaborations.