000K  utf8
1100  2023$c2023-11-03
1500  eng
2050  urn:nbn:de:gbv:ilm1-2023isc-127:3
2051  10.22032/dbt.58928
3000  Rother, Anne
3010  Arlinghaus, Julia
3010  Beyer, Christian
3010  Hasse, Alexander
3010  Noack, Benjamin
3010  Notni, Gunther
3010  Ragni, Marco
3010  Reißmann, Jan
3010  Spiliopoulou, Myra
3010  Zhang, Chen
4000  Human uncertainty in interaction with a machine$destablishing a reference dataset  [Rother, Anne]
4060  6 Seiten
4209  We investigate the task of malformed object classification in an industrial setting, where the term ‘malformed’ encompasses objects that are misshapen, distorted, corroded or broken. Recognizing whether such an object can be repaired, taken apart so that its components can be used otherwise, or dispatched for recycling, is a difficult classification task. Despite the progress of artificial intelligence for the classification of objects based on images, the classification of malformed objects still demands human involvement, because each such object is unique. Ideally, the intelligent machine should demand expert support only when it is uncertain about the class. But what if the human is also uncertain? Such a case must be recognized before being dealt with. Goal of this research thread is to establish a reference dataset on human uncertainty for such a classification problem and to derive indicators of uncertainty from sensory inputs. To this purpose, we designed an experiment for an object classification scenario where the uncertainty can be directly linked to the difficulty of labelling each object. By thus controlling uncertainty, we intend to build up a reference dataset and investigate how different sensory inputs can serve as uncertainty indicators for these data.
4950  https://doi.org/10.22032/dbt.58928$xR$3Volltext$534
4950  https://nbn-resolving.org/urn:nbn:de:gbv:ilm1-2023isc-127:3$xR$3Volltext$534
4961  https://www.db-thueringen.de/receive/dbt_mods_00058928
5051  620