Physical site security heavily relies on expert teams continually examining and testing security profiles for discovering potential vulnerabilities. These experts hypothesize scenario(s) of interest and conduct "red versus blue" simulated exercises where they execute tactics that might reveal possible dangers. Due to the intensive manpower required, video-game environments have become a widely-adopted mechanism for conducting these exercises with virtual agents replacing many of the human roles for quicker analyses. However, these agents either have limited capabilities or require several engineers to develop realistic behaviors. This paper documents an agent architecture and authoring suite that enables subject matter experts to easily build complex attack/response plans for agents to use within Dante, a 3D simulation platform for video-game-based training/analysis of force-on-force engagements. This work expands upon current trends in commercial video-game artificial intelligence (AI) architectures to build agent behaviors deemed qualitatively valid by security experts, with the runtime of these algorithms best suited for turn-based, strategy games.
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