Demo: dtControl: Decision Tree Learning Algorithms for Controller Representation

Ashok, Pranav; Jackermeier, Mathias; Jagtap, Pushpak; Kretinsky, Jan; Weininger, Maximilian and Zamani, Majid (2020): Demo: dtControl: Decision Tree Learning Algorithms for Controller Representation. In: Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control (HSCC2020) (Part of Cps-Iot Week)

Abstract

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].

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Language: English