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

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Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

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

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Abstract

V originále

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.