Talks and Poster Presentations (with Proceedings-Entry):

L. Nenzi, S. Silvetti, E. Bartocci, L. Bortolussi:
"A Robust Genetic Algorithm for Learning Temporal Specifications from Data";
Talk: Proc. of QEST 2018: the 15th International Conference on Quantitative Evaluation of Systems, Beijing, China; 2018-09-04 - 2018-09-07; in: "Proc. of QEST 2018: the 15th International Conference on Quantitative Evaluation of Systems", 11024 (2018), 323 - 338.

English abstract:
We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [9] and with a recently proposed decision-tree [8] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.