USING CASE-BASED REASONING TO ESTIMATE MANPOWER PERSONNEL TRAINING AND SAFETY REQUIREMENTS

THE OBJECTIVE IS TO DEVELOP A KNOWLEDGE-BASED ASSISTANT FOR ESTIMATING MANPOWER, PERSONNEL, TRAINING, AND SAFETY REQUIREMENTS FOR NEW SYSTEMS AND SUBSYSTEMS. A CASE-BASED REASONING (CBR) APPROACH TO KNOWLEDGE ELICITATION IS PROPOSED AS A SENSITIVE AND EFFECTIVE STRATEGY TO DERIVE AND REPRESENT CRITICAL KNOWLEDGE. PHASE I WILL DEMONSTRATE THE FEASIBILITY OF A CBR APPROACH THAT USES MPT&S DATA FROM PREVIOUS SYSTEMS AS THE BASIS FOR PREDICTIONS. STANDARD ARTIFICIAL INTELLIGENCE (AI) APPROACHES TO MPT&S ESTIMATION RUN INTO THE DIFFICULT OF HAVING TO MODEL THE COMPLEXITIES OF THE HARDWARE/SOFTWARE ELEMENTS ALONG WITH THE COMPLEXITIES OF THE HUMAN FACTORS. A CASE-BASED REASONING STRATEGY AVOIDS THIS PROBLEM BY RELYING ON MATCHES TO SIMILAR PROJECTS, ELIMINATING THE NEED TO REPRESENT WORLD KNOWLEDGE.