CAPTURING SPACE CREW REPRESENTATIONS OF CONTROL SYSTEMS WITH MULTIDIMENSIONAL SCALING

THE COMPLEXITY OF SPACECRAFT INFLIGHT OPERATIONAL SYSTEMS HAS LED TO A NEED AND DESIRE TO SUPPORT SYSTEM OPERATORS WITH INTELLIGENT TECHNOLOGIES. MODELING THE OPERATOR'S CONNITIVE PROCESSES IS NECESSARY FOR THE DEVELOPMENT OF THESE TECHNOLOGIES. WE PROPOSE AN INNOVATIVE TOOL AND METHODOLOGY BASED ON MULTIDIMENSIONAL SCALING (MDS) FOR CAPTURING THE COGNITIVE STRUCTURES OF SPACE CREW OPERATORS. OUR METHODOLOGY IS INNOVATIVE BECAUSE, UNLIKE EXISTING TECHNIQUES, IT IS QUANTITATIVE, FORMAL, REPLICABLE, AND ACCESSES THE UNDERLYING STRUCTURE OF AN OPERATOR'S CONCEPTUAL REPRESENTATION. EXISTING KNOWLEDGE ELICITATION TOOLS, IN CONTRAST, ARE QUALITATIVE, HEURISTIC, AND FOCUS ONLY ON AN OPERATOR'S PROCEDURAL KNOWLEDGE, AND CAN BE MORE USEFUL IN UNANTICIPATED OPERATIONAL CONDITIONS. AN AUTOMATED KNOWLEDGE ACQUISITION MDS-BASED SHELL WILL BE DEVELOPED IN PHASE II, BASED ON THE DESIGN AND TECHNIQUES DEVELOPED IN PHASE I.