Generating routes for entities in virtual environments, such as simulated vehicles or synthetic human characters, is a long-standing\nproblem, and route planning algorithms have been developed and studied for some time. Existing route planning algorithms,\nincluding the widely used A* algorithm, are generally intended to achieve optimality in some metric, such as minimum length or\nminimum time. Comparatively little attention has been given to route realism, defined as the similarity of the algorithm-generated\nroute to the route followed by real humans in the same terrain with the same constraints and goals. Commercial game engines\nhave seen increasing use as a context for research. To study route realism in a game engine, two developments were needed: a\nquantitative metric for measuring route realism and a game engine able to capture route data needed to compute the realism\nmetric. Enhancements for recording route data for both synthetic characters and human players were implemented within the\nUnreal Tournament 2004 game engine. A methodology for assessing the realism of routes and other behaviors using a quantitative\nmetric was developed. The enhanced Unreal Tournament 2004 game engine and the realism assessment methodology were tested\nby capturing data required to calculate a metric of route realism.
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