Abstract
Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and produce small and understandable controllers for discrete systems, complex continuous dynamics still pose a challenge. In particular, when the relationships between variables take more complex forms, such as polynomials, they cannot be obtained using the available DT learning procedures. In contrast, support vector machines provide a more powerful representation, capable of discovering many such relationships, but not in an explainable form. Therefore, we suggest to combine the two frameworks to obtain an understandable representation over richer, domain-relevant algebraic predicates. We demonstrate and evaluate the proposed method experimentally on established benchmarks.
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Funding
Open Access funding enabled and organized by Projekt DEAL. This research was funded in part by the German Research Foundation (DFG) projects 383882557 Statistical Unbounded Verification (SUV) and 427755713 Group-By Objectives in Probabilistic Verification (GOPro). This paper extends the tool dtControl [6] and through the synergy of algebraic, formal-methods and machine-learning approaches it increases the explainability of controllers, positioning itself into the STTT theme area Explanation Paradigms Leveraging Algebraic Intuition (ExPLAIn).
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Jüngermann, F., Křetínský, J. & Weininger, M. Algebraically explainable controllers: decision trees and support vector machines join forces. Int J Softw Tools Technol Transfer 25, 249–266 (2023). https://doi.org/10.1007/s10009-023-00716-z
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DOI: https://doi.org/10.1007/s10009-023-00716-z