AN A.I. APPROACH FOR TARGET RECOGNITION IN REAL BATTLE ENVIRONMENTS

RESEARCH IS NEEDED TO DEVELOP ALGORITHMS THAT ARE CAPABLE OF TARGET RECOGNITION IN REAL BATTLEFIELD ENVIRONMENTS. L.N.K. CORPORATION PROPOSES TO INVESTIGATE AN ARTIFICAL INTELLIGENCE TECHNIQUE WHOSE INFERENCE MECHANISM IS NON-DIRECTIONAL (BOTH GOAL-DIRECTED AND DATA-CONFIRMED) FOR RECOGNITION OF TARGETS IN IMAGES. THE ALGORITHM MEETS FOUR CRITERIA: 1) AMBIGUOUS INTERPRETATIONS ARE ALLOWED AND ARE DEVELOPED IN A BEST-FIRST MANNER. THUS, MULTIPLE CONTACTS OF TARGETS ARE RECOGNIZED IN A BEST-FIRST SEQUENCE. 2) GOAL-DRIVEN OR TOP-DOWN OPERATIONS ARE AVAILABLE TO MAKE HYPOTHESES FROM MODELS. 3) BOTTOMUP OPERATORS ARE AVAILBE TO INITIATE RECOGNITION, IF EVIDENCE IS PRESENT IN THE DATA. 4) THE ANALYSIS OF THE IMAGE CAN PRECEDE IN A NON-SEQUENTIAL FASHION THROUGH THE IMAGE, TO OPTIMIZE RELIABILITY OF RECOGNITION OF PARTIALLY OCCULUDED OBJECTS IS POSSIBLE BECAUSE AN OBJECT IS MODELED BY A HIERARCHY OF SUBSTRUCTURES. A PARALLEL IMPLEMENTATION OF THE