ARCH-COMP 2024 Category Report: Falsification

This report presents the results from the falsification category of the 2024 competition in the Applied Verification for Continuous and Hybrid Systems (ARCH) workshop. The report summarizes the competition rules and settings, the benchmark models for the tool comparison, and provides background on the participating teams and tools. Finally, it presents and discusses the results of the competition.

Authors

Publication Details

Journal
EPiC series in computing
Published
2024-10-10
DOI
https://doi.org/10.29007/hgfv
Citations
7
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
2.08

Funders

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article

ARCH-COMP 2024 Category Report: Falsification

Surdeep Chotaliya, Claudio Menghi, Paolo Arcaini, Laura Nenzi et al.
7 citations
EPiC series in computing
Adversarial Robustness in Machine Learning
2.08
article

ARCH-COMP 2024 Category Report: Falsification

Surdeep Chotaliya, Claudio Menghi, Paolo Arcaini, Laura Nenzi, Valentin Soloviev, Masaki Waga, Tanmay Khandait, Iván Porres, Ethan Lew, Giulia Pedrielli, Georgios Fainekos, Jarkko Peltomäki, Michele Loreti, Federico Formica, Ennio Visconti, R. L. Ray, Zhenya Zhang, A. Kundu, Abdelrahman Hekal
article en
7 citations

Abstract

This report presents the results from the falsification category of the 2024 competition in the Applied Verification for Continuous and Hybrid Systems (ARCH) workshop. The report summarizes the competition rules and settings, the benchmark models for the tool comparison, and provides background on the participating teams and tools. Finally, it presents and discusses the results of the competition.

EPiC series in computingVol. 103
National Science Foundation, European Commission, Austrian Science Fund, Technische Universität Wien, Universität Salzburg, Electronic Components and Systems for European Leadership, Alliance de recherche numérique du Canada, Defense Advanced Research Projects Agency, Japan Society for the Promotion of Science, Division of Civil, Mechanical and Manufacturing Innovation, Core Research for Evolutional Science and Technology, Exploratory Research for Advanced Technology, JST-Mirai Program, ACT-X
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Adversarial Robustness in Machine Learning
2.08
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