Talks and Poster Presentations (with Proceedings-Entry):
D. Phan, N. Paoletti, T. Zhang, R. Grosu, S. Smolka, S. Stoller:
"Neural State Classification for Hybrid Systems";
Talk: Proc. of ATVA 2018: the 16th International Symposium on Automated Technology for Verification and Analysis,
Los Angeles, CA, USA;
- 2018-10-10; in: "Proc. of ATVA 2018: the 16th International Symposium on Automated Technology for Verification and Analysis",
We introduce the State Classification Problem (SCP) for continuous and hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique. SCP generalizes the model checking problem as it entails classifying each state s of a hybrid automaton as either positive or negative, depending on whether or not s satisfies a given time-bounded reachability specification. This is an interesting problem in its own right, which NSC solves using machine-learning techniques, Deep Neural Networks in particular. State classifiers produced by NSC tend to be very efficient (run in constant time and space), but may be subject to classification errors. To quantify and mitigate such errors, our approach comprises: i) techniques for certifying, with statistical guarantees, that an NSC classifier meets given accuracy levels; ii) tuning techniques, including a novel technique based on adversarial sampling, that can virtually eliminate false negatives (positive states classified as negative), thereby making the classifier more conservative. We have applied NSC to six nonlinear hybrid system benchmarks, achieving an accuracy of 99.25% to 99.98%, and a false-negative rate of 0.0033 to 0, which we further reduced to 0.0015 to 0 after tuning the classifier. We believe that this level of accuracy is acceptable in many practical applications, and that these results demonstrate the promise of the NSC approach.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Created from the Publication Database of the Vienna University of Technology.