{'fraction_non_alphanumeric': 0.022894521668029435, 'fraction_numerical': 0.0, 'mean_word_length': 5.276923076923077, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Classifier-Based Supervisory Control with Application to Threat Engagement

We propose a data-driven supervisory method to determine actions, in real-time, for systems with a binary success/failure outcome. This approach consists of two steps. First, a high-fidelity system model is used offline to train a classifier, which acts as a quick-to-evaluate approximation of the system. Then, the classifier is used online to select an action based on the scenario encountered. The method also returns an approximate probability of success which can then be used to inform follow-on decisions. We apply this method to problems where an interceptor missile engages a threat headed towards an asset. The interceptor’s supervisory actions may include selecting parameters in the guidance and control laws, setting tunable initial conditions, and determining other details about how the interceptor will engage the threat. Specifically, the proposed method is demonstrated using the case study of a planar engagement between an interceptor and a threat, with the interceptor launch angle and autopilot crossover frequency as actionable parameters. For this case study, the proposed method outperforms an alternative baseline action.