Publication details
dtControl: decision tree learning algorithms for controller representation
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Proceedings |
| Conference | HSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control, Sydney, New South Wales, Australia, April 21-24, 2020 |
| MU Faculty or unit | |
| Citation | |
| Doi | https://doi.org/10.1145/3365365.3383468 |
| Description | 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]. |