Open AccessARCH-COMP20 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
Author(s) -
Taylor T. Johnson,
Diego Manzanas Lopez,
Patrick Musau,
Hoang-Dung Tran,
Elena Botoeva,
Francesco Leofante,
Abbas Maleki,
Chelsea Sidrane,
Jiameng Fan,
Chao Huang
Publication year - 2020
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.21
H-Index - 7
ISSN - 2398-7340
DOI - 10.29007/9xgv
Subject(s) - artificial neural network , artificial intelligence , feed forward , hybrid system , ranking (information retrieval) , control system , computer science , benchmark (surveying) , feedforward neural network , engineering , hybrid learning , machine learning , control engineering , electrical engineering , geodesy , geography
This report presents the results of a friendly competition for formal verification of continuous and hybrid systems with artificial intelligence (AI) components. Specifically, machine learning (ML) components in cyber-physical systems (CPS), such as feedforward neural networks used as feedback controllers in closed-loop systems are considered, which is a class of systems classically known as intelligent control systems, or in more modern and specific terms, neural network control systems (NNCS). We more broadly refer to this category as AI and NNCS (AINNCS). The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020. In the second edition of this AINNCS category at ARCH-COMP, four tools have been applied to solve seven different benchmark problems, (in alphabetical order): NNV, OVERT, ReachNN*, and VenMAS. This report is a snapshot of the current landscape of tools and the types of benchmarks for which these tools are suited. Due to the diversity of problems, lack of a shared hardware platform, and the early stage of the competition, we are not ranking tools in terms of performance, yet the presented results probably provide the most complete assessment of current tools for safety verification of NNCS.