Algebraically explainable controllers: decision trees and support vector machines join forces

JUENGERMANN, Florian, Jan KŘETÍNSKÝ and Maximilian WEININGER. Algebraically explainable controllers: decision trees and support vector machines join forces. 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.

Basic information

• Original name: Algebraically explainable controllers: decision trees and support vector machines join forces
• Authors: JUENGERMANN, Florian, Jan KŘETÍNSKÝ (203 Czech Republic, belonging to the institution) and Maximilian WEININGER.
• Edition: International Journal on Software Tools for Technology Transfer, HEIDELBERG, SPRINGER HEIDELBERG, 2023, 1433-2779.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Germany
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 1.500 in 2022
• RIV identification code: RIV/00216224:14330/23:00133937
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1007/s10009-023-00716-z
• UT WoS: 001045591500001
• Keywords in English: Controller representation; Explainability; Synthesis; Decision tree
• Tags: International impact, Reviewed

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.