ARTIFICIAL INTELLIGENCE FOR COMMAND AND CONTROL

THIS PROPOSAL IS A PLAN FOR A PROOF OF CONCEPT DEMONSTRATION OF AN ARTIFICIAL INTELLIGENCE-BASED DECISION AID TO INTEGRATE PLANNING AND ANALYSIS OF AIR DEFENSE NETWORKS ON A SINGLE WORKSTATION. PROCEDURES FOR PLANNING AND ANALYZING AN AIR DEFENSE NETWORK ARE TIME AND LABOR INTENSIVE. THESE PROCEDURES CONSTITUTE ONE OF THE MOST DIFFICULT ASPECTS OF OPERATIONAL PLANNING BECAUSE OF TRANSITORY AIRSPACE EVENTS. THE PROPOSED SYSTEM INTEGRATES DECISION MAKING SUPPORT ROUTINES, HIGH-RESOLUTION TERRAIN AND FEATURE DATABASES WEAPONS SYSTEM CONSTRAINTS AND CAPABILITIES, AND DEPLOYMENT DOCTRINES WITH ADVANCED COMPUTING TECHNOLOGIES TO DRAMATICALLY REDUCE THE OPERATIONAL ANALYSIS, SITUATION ASSESSMENT, AND DECISION MAKING CYCLE. THE SYSTEM WILL DEVELOP DECISION SUPPORT TEMPLATES FOR AIR DEFENSE COMMAND AND CONTROL ORGANS THROUGH A LAYERED REASONING PROCESS. RULE-DRIVEN GRAPHICAL ALGORITHMS WILL BE APPLIED TO PROVIDE FAST AND INTELLIGENT AUTOMATED SITE SELECTION AND EVALUATION PROCESSES. PATHFINDING AND GRAPHICAL/REGION GROWING ALGORITHMS WILL EVALUATE INTERSITE ROUTES. POSSIBLE TECHNIQUES FOR OPTIMIZING SITE COMBINATIONS INTO AIR DEFENSE NETWORKS INCLUDE DIRECTED SEARCH, TREE-PRUMING ALGORITHMS, RELAXATION TECHNIQUES, AND ARTIFICIAL NEURAL NETWORKS.