TY - GEN
T1 - dtControl 2.0
T2 - 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021 Held as Part of 24th European Joint Conferences on Theory and Practice of Software, ETAPS 2021
AU - Ashok, Pranav
AU - Jackermeier, Mathias
AU - Křetínský, Jan
AU - Weinhuber, Christoph
AU - Weininger, Maximilian
AU - Yadav, Mayank
N1 - Publisher Copyright:
© The Author(s) 2021.
PY - 2021
Y1 - 2021
N2 - Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make the strategy more explainable. The recent tool dtControl had provided pipelines with tools supporting strategy synthesis for hybrid systems, such as SCOTS and Uppaal Stratego. We present dtControl 2.0, a new version with several fundamentally novel features. Most importantly, the user can now provide domain knowledge to be exploited in the decision tree learning process and can also interactively steer the process based on the dynamically provided information. To this end, we also provide a graphical user interface. It allows for inspection and re-computation of parts of the result, suggesting as well as receiving advice on predicates, and visual simulation of the decision-making process. Besides, we interface model checkers of probabilistic systems, namely STORM and PRISM and provide dedicated support for categorical enumeration-type state variables. Consequently, the controllers are more explainable and smaller.
AB - Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make the strategy more explainable. The recent tool dtControl had provided pipelines with tools supporting strategy synthesis for hybrid systems, such as SCOTS and Uppaal Stratego. We present dtControl 2.0, a new version with several fundamentally novel features. Most importantly, the user can now provide domain knowledge to be exploited in the decision tree learning process and can also interactively steer the process based on the dynamically provided information. To this end, we also provide a graphical user interface. It allows for inspection and re-computation of parts of the result, suggesting as well as receiving advice on predicates, and visual simulation of the decision-making process. Besides, we interface model checkers of probabilistic systems, namely STORM and PRISM and provide dedicated support for categorical enumeration-type state variables. Consequently, the controllers are more explainable and smaller.
KW - Controller representation
KW - Decision Tree
KW - Explainable Learning
KW - Hybrid systems
KW - Markov Decision Process
KW - Probabilistic Model Checking
KW - Strategy representation
UR - http://www.scopus.com/inward/record.url?scp=85135374612&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72013-117
DO - 10.1007/978-3-030-72013-117
M3 - Conference contribution
AN - SCOPUS:85135374612
SN - 9783030720124
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 326
EP - 345
BT - Tools and Algorithms for the Construction and Analysis of Systems - 27th International Conference, TACAS 2021 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021
A2 - Groote, Jan Friso
A2 - Larsen, Kim Guldstrand
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 March 2021 through 1 April 2021
ER -