Combining optimal high-level planning with low-level behaviors has usually been accomplished through two separate mechanisms. Typically, both of these mechanisms have relied upon heuristic approaches to control the behaviors of simulated agents to achieve mission goals. Recent research into evolving optimal high-level tactical behaviors for simulated vehicles proved quite fruitful even when heuristics were utilized to navigate lowlevel terrain. Evolutionary programming was used to optimally control computer generated forces (CGFs) on two opposing teams in highly dynamic environments. Tactical courses of action were learned adaptively for individual vehicles as well as for higher-level aggregations (i.e., platoons). Evolutionary updates of behavioral plans incorporated dynamic changes in the developing situation and the sensed environment.
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