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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 509-512
Yoo Jin Lim , Server Technologies Division, Oracle Korea, Seoul, Republic of Korea
Gwangui Hong , SW Infra Development Section, KFTC, Seoul, Republic of Korea
Donghwan Shin , School of Comuting, KAIST, Daejeon, Republic of Korea
Eunkyoung Jee , School of Comuting, KAIST, Daejeon, Republic of Korea
Doo-Hwan Bae , School of Comuting, KAIST, Daejeon, Republic of Korea
ABSTRACT
Dynamically adaptive multi-agent systems (DAMS) consist of multiple agents that adapt to changing system and environmental conditions in order to achieve collaborative goals. As DAMS are found in applications across various domains, ensuring the correct and safe adaptations of DAMS has become more important. Formal verification techniques such as model checking present a promising approach to guaranteeing the correctness of a software system with respect to certain system requirements. Previous works on formal verification for dynamically adaptive system or multi-agent system, however, have not addressed the runtime and collaborative nature inherent to DAMS operations. This work proposes a runtime verification framework for DAMS (DAMS-RV) based on an adaptive feedback loop, which is activated for each adaptation that system makes after a change in the system or environment. The proposed framework is described using a collaborative nurse agent system as a running example. A case study with an application scenario provides insights into how DAMS-RV can serve as a feasible and effective framework for DAMS verification.
INDEX TERMS
Collaboration, Runtime, Context, Monitoring, Model checking, Multi-agent systems
CITATION
Yoo Jin Lim, Gwangui Hong, Donghwan Shin, Eunkyoung Jee, Doo-Hwan Bae, "A runtime verification framework for dynamically adaptive multi-agent systems", 2016 International Conference on Big Data and Smart Computing (BigComp), vol. 00, no. , pp. 509-512, 2016, doi:10.1109/BIGCOMP.2016.7425981
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