dtControl: decision tree learning algorithms for controller
representation

D 2020

dtControl: decision tree learning algorithms for controller representation

ASHOK, Pranav; Mathias JACKERMEIER; Pushpak JAGTAP; Jan KŘETÍNSKÝ; Maximilian WEININGER et al.

Základní údaje

Originální název

dtControl: decision tree learning algorithms for controller representation

Autoři

ASHOK, Pranav; Mathias JACKERMEIER; Pushpak JAGTAP; Jan KŘETÍNSKÝ; Maximilian WEININGER a Majid ZAMANI

Vydání

HSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control, Sydney, New South Wales, Australia, April 21-24, 2020, od s. 1-2, 2 s. 2020

Nakladatel

ACM

Další údaje

Typ výsledku

Stať ve sborníku

Označené pro přenos do RIV

Organizační jednotka

Fakulta informatiky

ISBN

9781450370189

DOI

https://doi.org/10.1145/3365365.3383468

Změněno: 17. 3. 2025 14:43, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision tree representations are smaller and more explainable. We present dtControl, an easily extensible tool offering a wide variety of algorithms for representing memoryless controllers as decision trees. We highlight that the trees produced by dtControl are often very concise with a single-digit number of decision nodes. This demo is based on our tool paper [1].

Citovat

ASHOK, Pranav; Mathias JACKERMEIER; Pushpak JAGTAP; Jan KŘETÍNSKÝ; Maximilian WEININGER a Majid ZAMANI. dtControl: decision tree learning algorithms for controller representation. In HSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control, Sydney, New South Wales, Australia, April 21-24, 2020. ACM, 2020, s. 1-2. ISBN 9781450370189. Dostupné z: https://doi.org/10.1145/3365365.3383468.

@inproceedings{2484793,
   author = {Ashok, Pranav and Jackermeier, Mathias and Jagtap, Pushpak and Křetínský, Jan and Weininger, Maximilian and Zamani, Majid},
   booktitle = {HSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control, Sydney, New South Wales, Australia, April 21-24, 2020},
   doi = {https://doi.org/10.1145/3365365.3383468},
   isbn = {9781450370189},
   pages = {1-2},
   publisher = {ACM},
   title = {dtControl: decision tree learning algorithms for controller representation},
   year = {2020}
}

TY  - CONF
ID  - 2484793
AU  - Ashok, Pranav - Jackermeier, Mathias - Jagtap, Pushpak - Křetínský, Jan - Weininger, Maximilian - Zamani, Majid
PY  - 2020
TI  - dtControl: decision tree learning algorithms for controller representation
PB  - ACM
SN  - 9781450370189
N2  - Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision tree representations are smaller and more explainable. We present dtControl, an easily extensible tool offering a wide variety of algorithms for representing memoryless controllers as decision trees. We highlight that the trees produced by dtControl are often very concise with a single-digit number of decision nodes. This demo is based on our tool paper [1].
ER  -

ASHOK, Pranav; Mathias JACKERMEIER; Pushpak JAGTAP; Jan KŘETÍNSKÝ; Maximilian WEININGER a Majid ZAMANI. dtControl: decision tree learning algorithms for controller representation. In \textit{HSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control, Sydney, New South Wales, Australia, April 21-24, 2020}. ACM, 2020, s.~1-2. ISBN~9781450370189. Dostupné z: https://doi.org/10.1145/3365365.3383468.

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