Design of an Intelligent Military Recruitment Support System Based on Certainty Factor and Forward Chaining Inference

Abstract

Military recruitment demands careful evaluation across multiple dimensions, including administrative eligibility, physical fitness, cognitive ability, and psychological readiness. This paper presents the development of an intelligent decision support system (DSS) designed to assist and enhance recruitment decisions in the military sector. Leveraging a rule-based expert system, the DSS applies the Certainty Factor (CF) model to manage uncertainty and combines it with a Forward Chaining inference mechanism for explainable reasoning. The system was built with guidance from military domain experts and tested through simulations using 20 candidate profiles. Results indicate strong alignment with human expert judgment, achieving 87% accuracy and delivering transparent recommendations based on structured rules and confidence scores. Unlike conventional black-box models, the DSS offers full traceability for each decision made, ensuring accountability and consistency-qualities essential in defense-related processes. Beyond its current capabilities, the system is designed to be scalable and adaptable. It can accommodate evolving recruitment policies and integrate additional evaluation criteria, such as biometric data. Although current CF weights rely on expert input, future development may involve dynamic rule optimization through machine learning or feedback mechanisms. In conclusion, the proposed DSS serves as a practical and explainable tool that bridges expert knowledge with intelligent automation. It offers a forward-looking approach to improving fairness, efficiency, and transparency in military recruitment processes.