We have developed a game-theory driven decision-support tool that builds probabilistic game trees automatically from user-defined actions, rules, and states. The result of evaluating the paths in the game tree is a series of decisions which forms a decision-path representing an ε-Nash-Equilibrium. The algorithm uses certainty-equivalents to handle trade-offs between expected rewards and risks, effectively modeling the probabilistic game tree as deterministic. The resulting decision-paths correspond to player actions in the scenario. These sets of actions can be used as search patterns against a real-world database. A match to one of these patterns indicates an instance of novel behavior patterns generated by the game-theory driven decision support tool. This particular paradigm could be applied in any domain that requires anticipating and responding to adversarial agents with uncertainty, from mission planning to emergency responders to systems configuration.