A Graph-Based Transformation Reduction to Reach UPPAAL States Faster

Rinast, Jonas; Schupp, Sibylle; Gollmann, Dieter

doi:10.1007/978-3-319-06410-9_37

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…time horizon, and by frequently updating these guarantees at runtime. In this category, Rinast et al [58] presents an OMC technique for timed systems implemented in UPPAAL [9] based on graphbased techniques for reconstructing the model state space from the real-world system state. OMC for hybrid automata (HA) models is considered by [53], where estimation of a linear HA from observations and time-bounded verification are applied at runtime to a laser tracheotomy case study.…”

Section: Related Workmentioning

confidence: 99%

How to Learn a Model Checker

Phan,

Grosu,

Paoletti

et al. 2017

Preprint

View full text Add to dashboard Cite

We show how machine-learning techniques, particularly neural networks, offer a very effective and highly efficient solution to the approximate model-checking problem for continuous and hybrid systems, a solution where the general-purpose model checker is replaced by a model-specific classifier trained by sampling model trajectories. To the best of our knowledge, we are the first to establish this link from machine learning to model checking. Our method comprises a pipeline of analysis techniques for estimating and obtaining statistical guarantees on the classifier's prediction performance, as well as tuning techniques to improve such performance. Our experimental evaluation considers the time-bounded reachability problem for three well-established benchmarks in the hybrid systems community. On these examples, we achieve an accuracy of 99.82% to 100% and a false-negative rate (incorrectly predicting that unsafe states are not reachable from a given state) of 0.0007 to 0. We believe that this level of accuracy is acceptable in many practical applications and we show how the approximate model checker can be made more conservative by tuning the classifier through further training and selection of the classification threshold.

show abstract

Section: Related Workmentioning

confidence: 99%