Strategies for Safety Goal Decomposition for Neural Networks





Faculty/Professorship: Cognitive Systems  
Author(s): Schwalbe, Gesina  ; Schels, Martin
Corporate Body: ACM Computer Science in Cars Symposium (CSCS), 3, 2019, Kaiserslautern
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2020
Pages: 3
Language(s): English
DOI: 10.20378/irb-47274
Licence: Creative Commons - CC BY-NC-SA - Attribution - NonCommercial - ShareAlike 4.0 International 
URL: https://cscs19.cispa.saarland/files/cscs19_came...
URN: urn:nbn:de:bvb:473-irb-472744
Abstract: 
Neural networks (NNs) have become a key technology for solving highly complex tasks, and require integration into future safety argumentations. New safety relevant aspects introduced by NN based algorithms are: representativity of test cases, robustness, inner representation and logic, and failure detection for NNs. In this paper, a general argumentation structure for safety cases respecting these four aspects is proposed together with possible sources of evidence.
SWD Keywords: Neuronales Netz ; Beweisführung ; Dekomposition ; Sicherheit
Keywords: neural network, safety argumentation, safety goal decomposition
DDC Classification: 004 Computer science  
RVK Classification: ST 301   
Document Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/47274
Release Date: 3. July 2020

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