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Anomaly Detection and XAI Concepts in Swarm Intelligence

 
: Anneken, Mathias; Veerappa, Manjunatha; Burkart, Nadia

:
Volltext urn:nbn:de:0011-n-6388357 (139 KByte PDF)
MD5 Fingerprint: 2c6c05990f4bdb54b8a9ae797af460b6
Erstellt am: 20.8.2021


North Atlantic Treaty Organization -NATO-, Brussels:
PRE-RELEASE: Situation Awareness of Swarms and Autonomous Systems
Brussels: NATO STO, 2021 (STO-MP-SCI 341)
ISBN: 978-92-837-2349-3
Paper MP-SCI-341-05, 8 S.
Englisch
Aufsatz in Buch, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
For human operators in swarm intelligence, decision support in critical situations is crucial. The large amount of data shared by the autonomous systems, can easily make the human decision makers too overwhelmed by it and hence there is a need for support in analysing the data in an intelligent way. For this purpose, automatic systems for assessing situations and indicating suspicious behaviour or statistical outliers is employed. This strengthen their situation awareness as well as decrease the work load. Therefore, in this work, we emphasize that the data fusion services developed for detecting anomalies in surveillance tasks, e.g. in the maritime domain, can be adapted to support operators in swarm intelligence. Furthermore, in order to make the behaviour of the swarm and the results of the data fusion services understandable to the human operator, explainable artificial intelligence (XAI) concepts are introduced. This makes the autonomous system’s behaviour more intelligible and understandable to humans by providing explanations for certain decisions.

: http://publica.fraunhofer.de/dokumente/N-638835.html