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Tackling Challenges of Robustness Measures for Autonomous Agent Collaboration in Open Multi-Agent Systems

Jin, David; Kannengießer, Niclas ORCID iD icon; Sturm, Benjamin; Sunyaev, Ali


Open multi-agent systems (OMASs) allow autonomous agents (AAs) to collaborate in coalitions to accomplish complex tasks (e.g., swarm robots exploring new terrain). In OMASs, AAs can arbitrarily join and leave the network. Thus, AAs must often collaborate with unknown AAs that may corrupt coalitions, leading to less robust systems. However, measures to improve robustness of OMASs are subject to challenges, decreasing their effectiveness. To understand how to improve coalition robustness in OMASs and address challenges of existing robustness measures, we carried out a literature review and revealed three types of robustness measures (i.e., collaboration coordination, normative control, and reliability prediction). Moreover, we found 21 challenges for the identified robustness measures and 24 corresponding solutions. By carrying out this literature review, we forge new connections between existing measures and identify challenges and measures that apply to multiple existing measures. Hereby, our work supports more robust collaborations between AAs in open systems

Preprint §
DOI: 10.5445/IR/1000137879
Veröffentlicht am 15.10.2021
DOI: 10.24251/HICSS.2022.911
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 01.2022
Sprache Englisch
Identifikator ISBN: 978-0-9981331-5-7
KITopen-ID: 1000137879
HGF-Programm 46.23.03 (POF IV, LK 01) Engineering Security for Mobility Systems
Weitere HGF-Programme 46.23.01 (POF IV, LK 01) Methods for Engineering Secure Systems
Erschienen in Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS), Maui, Hawaii, 03.01.2022-07.01.2022
Veranstaltung 55th Hawaii International Conference on System Sciences (HICSS 2022), Online, 03.01.2022 – 07.01.2022
Seiten 7585-7594
Schlagwörter open multi-agent systems, robustness, distributed artificial intelligence
Nachgewiesen in Dimensions
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