Regression Trees for System Models and Prediction
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4th Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis (OVERLAY 2022)
Today's systems are highly complex and often appear as black-box systems because of unknown internal functionalities. Thus, retrieving a model of a system is possible only based on observations of the system. We explore the usage of regression trees to learn a model of a complex system with continuous signals. Our approach for learning a regression tree uses observed inputs and outputs of a system from bounded history. We describe how to construct such a model and analyze the accuracy of predictions. Results show the applicability of regression tree models for continuous systems.