Abstract. We introduce a novel approach to the smart execution of scenario-based models of reactive systems, such as those resulting from the multi-modal inter-object language of live sequence charts (LSCs). Our approach finds multiple execution paths from a given state of the system, and allows the user to interactively traverse them. The method is based on translating the problem of finding a superstep of execution into a problem in the AI planning domain, and issuing a known planning algorithm, which we have had to modify and strengthen for our purposes.
Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models.This work introduces VERIGB, a tool for quantifying the robustness of gradient boosted models. VERIGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model's robustness. We extensively evaluate VERIGB on publicly available datasets and demonstrate a capability for verifying large models. Finally, we show that some model configurations tend to be inherently more robust than others.
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