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A key drawback of the current generation of artificial decision-makers isthat they do not adapt well to changes in unexpected situations. This paperaddresses the situation in which an AI for aerial dog fighting, with tunableparameters that govern its behavior, will optimize behavior with respect to anobjective function that must be evaluated and learned through simulations. Oncethis objective function has been modeled, the agent can then choose its desiredbehavior in different situations. Bayesian optimization with a Gaussian Processsurrogate is used as the method for investigating the objective function. Onekey benefit is that during optimization the Gaussian Process learns a globalestimate of the true objective function, with predicted outcomes and astatistical measure of confidence in areas that haven't been investigated yet.However, standard Bayesian optimization does not perform consistently orprovide an accurate Gaussian Process surrogate function for highly volatileobjective functions. We treat these problems by introducing a novel samplingtechnique called Hybrid Repeat/Multi-point Sampling. This technique gives theAI ability to learn optimum behaviors in a highly uncertain environment. Moreimportantly, it not only improves the reliability of the optimization, but alsocreates a better model of the entire objective surface. With this improvedmodel the agent is equipped to better adapt behaviors.

Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions