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@article{2392231, author = {Juengermann, Florian and Křetínský, Jan and Weininger, Maximilian}, article_location = {HEIDELBERG}, article_number = {3}, doi = {http://dx.doi.org/10.1007/s10009-023-00716-z}, keywords = {Controller representation; Explainability; Synthesis; Decision tree}, language = {eng}, issn = {1433-2779}, journal = {International Journal on Software Tools for Technology Transfer}, title = {Algebraically explainable controllers: decision trees and support vector machines join forces}, url = {https://doi.org/10.1007/s10009-023-00716-z}, volume = {25}, year = {2023} }
TY - JOUR ID - 2392231 AU - Juengermann, Florian - Křetínský, Jan - Weininger, Maximilian PY - 2023 TI - Algebraically explainable controllers: decision trees and support vector machines join forces JF - International Journal on Software Tools for Technology Transfer VL - 25 IS - 3 SP - 249-266 EP - 249-266 PB - SPRINGER HEIDELBERG SN - 14332779 KW - Controller representation KW - Explainability KW - Synthesis KW - Decision tree UR - https://doi.org/10.1007/s10009-023-00716-z N2 - 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. ER -
JUENGERMANN, Florian, Jan KŘETÍNSKÝ and Maximilian WEININGER. Algebraically explainable controllers: decision trees and support vector machines join forces. \textit{International Journal on Software Tools for Technology Transfer}. HEIDELBERG: SPRINGER HEIDELBERG, 2023, vol.~25, No~3, p.~249-266. ISSN~1433-2779. Available from: https://dx.doi.org/10.1007/s10009-023-00716-z.
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