dtControl: decision tree learning algorithms for controller representation
By Pranav Ashok, Mathias Jackermeier, Pushpak Jagtap, Jan Křetínský, Maximilian Weininger, Majid Zamani. 2020
36 citations recorded by the source corpus.
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 trees are smaller and more explainable. We present dtControl, an easily extensible tool for representing memoryless controllers as decision trees. We give a comprehensive evaluation of various decision tree learning algorithms applied to 10 case studies arising out of correct-by-construction controller synthesis. These algorithms include two new techniques, one for using arbitrary linear binary classifiers in the decision tree learning, and one novel approach for determinizing controllers during the decision tree construction. In particular the latter turns out to be extremely efficient, yielding decision trees with a single-digit number of decision nodes on 5 of the case studies.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering
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In Artificial Intelligence
- Emmanuel Hebrard
- Peter James Stuckey
- C. Leckie
- Emir Demirovi'c
- Jeffrey Chan
- Anna Lukina
- James Bailey
- Kotagiri Ramamohanarao
In Computational Theory and Mathematics
- Jeffrey Chan
- Anna Lukina
- James Bailey
- Peter James Stuckey
- C. Leckie
- Kotagiri Ramamohanarao
- Emmanuel Hebrard
- Emir Demirovi'c
In Control and Systems Engineering
Citations in the corpus
- MurTree: Optimal Decision Trees via Dynamic Programming and Search (2020, background): “In some applications, the goal is to optimise the size of the decision tree representing a given controller to save memory for embedded devices (Ashok et al. (2020)).”
- MurTree: Optimal Classification Trees via Dynamic Programming and Search (2020, background): “In some applications, the goal is to optimise the size of the decision tree representing a given controller to save memory for embedded devices (Ashok et al. (2020)).”