EVOLUTIONARY PROGRAMMING APPLIED TO PATTERN RECOGNITION

THE NATURE OF TACTICAL ARMY WARFARE IS MOVING TOWARD GREATER COMPLEXITY AS THE COMMAND STRUCTURE IS REQUIRED TO DEAL WITH INCREASING AMOUNTS OF INFORMATION IN NEAR REAL TIME THAT MUST BE USED AS A BASIS FOR CRITICAL DECISION MAKING. THE TECHNOLOGY BEING DEVELOPED IN THE ARTIFICIAL INTELLIGENCE COMMUNITY IS SHOWING CONSIDERABLE PROGRESS TO PROVIDE THE TECHNIQUES TO HELP THE BATTLEFIELD COMMANDER MEET THIS CHALLENGE. PROCESSING AREAS INCLUDE EXPERT SYSTEMS AND NEURAL NETWORKS. PHASE I DEMONSTRATES APPLICATION OF TECHNIQUES DERIVED FROM NATURAL SELECTION AND GENETICS (I.E., THE GENETIC ALGORITHM) TO EVOLVE FINITE STATE MACHINES THAT LEARN TO RECOGNIZE PATTERNS FROM PAST OBSERVATIONS. THE APPROACH WILL UE SIMPLE PATTERNS AND COMPARE RESULTS WITH A NEURAL NETWORK APPROACH. FINITE STATE MACHINES ARE A VERY SIMPLE WAY TO TRANSFORM DIGITAL DATA BY MAPPING THE OBSERVED DATA INTO PREDICTED DATA BY TRANSITIONS BETWEEN A FINITE NUMER OF INTERNAL STATES. THE INTERNAL OPERATION OF THE FINITE STATE MACHINE IS CARRIED OUT IN SUCH A WAY AS TO CHARACTERIZE INFORMATION CONTENT. THEIR SIMPLE STRUCTURE ALONG WITH THE GENETIC ALGORITHM IS AMENABLE TO BEING IMPLEMENTED IN EFFICIENT PARALLEL PROCESSING ARCHITECTURES.