@inproceedings{DBLP:conf/prima/DellAnnaDD18,
  author    = {Davide Dell'Anna and
               Mehdi Dastani and
               Fabiano Dalpiaz},
  title     = {Runtime Norm Revision Using Bayesian Networks},
  booktitle = {Proceedings of the 21st International Conference on Principles and Practice of Multi-Agent Systems, {PRIMA} 2018},
  volume    = {11224},
  pages     = {279--295},
  year      = {2018},
  url_Link       = {https://doi.org/10.1007/978-3-030-03098-8\_17},
  url_Paper      = {2018_PRIMA/PRIMA18_DellAnna.pdf},
  url_Slides= {2018_PRIMA/PRIMA18_DellAnna_Slides.pdf},
  url_Poster= {2018_PRIMA/PRIMA18_DellAnna_Poster.pdf},
  doi       = {10.1007/978-3-030-03098-8\_17},
  keywords  = {Norm Revision, Norms, Bayesian Networks, Runtime, MAS, Multi-Agent Systems, Data Driven Supervision Of Autonomous Systems},
  timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/prima/DellAnnaDD18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  abstract = {To guarantee the overall desirable objectives of multiagent systems, the behavior of individual agents should be controlled and coordinated. Runtime norm enforcement is a mechanism to control and coordinate the behavior of agents at runtime without limiting their autonomy. However, due to the dynamicity and uncertainty involved in the agents' environments, the enforced norms can sometimes be ineffective. In this paper we propose a runtime supervision mechanism that automatically revises norms when their enforcement appears to be ineffective. The decision to revise norms is taken based on a Bayesian Network that gives information about the likelihood of achieving the overall desirable system objectives by enforcing the norms. Norms can be revised in three ways: relaxation, strengthening and alteration. We evaluate the supervision mechanism based on an urban smart traffic simulation.}
}

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We evaluate the supervision mechanism based on an urban smart traffic simulation.","bibtex":"@inproceedings{DBLP:conf/prima/DellAnnaDD18,\n author = {Davide Dell'Anna and\n Mehdi Dastani and\n Fabiano Dalpiaz},\n title = {Runtime Norm Revision Using Bayesian Networks},\n booktitle = {Proceedings of the 21st International Conference on Principles and Practice of Multi-Agent Systems, {PRIMA} 2018},\n volume = {11224},\n pages = {279--295},\n year = {2018},\n url_Link = {https://doi.org/10.1007/978-3-030-03098-8\\_17},\n url_Paper = {2018_PRIMA/PRIMA18_DellAnna.pdf},\n url_Slides= {2018_PRIMA/PRIMA18_DellAnna_Slides.pdf},\n url_Poster= {2018_PRIMA/PRIMA18_DellAnna_Poster.pdf},\n doi = {10.1007/978-3-030-03098-8\\_17},\n keywords = {Norm Revision, Norms, Bayesian Networks, Runtime, MAS, Multi-Agent Systems, Data Driven Supervision Of Autonomous Systems},\n timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},\n biburl = {https://dblp.org/rec/conf/prima/DellAnnaDD18.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n abstract = {To guarantee the overall desirable objectives of multiagent systems, the behavior of individual agents should be controlled and coordinated. Runtime norm enforcement is a mechanism to control and coordinate the behavior of agents at runtime without limiting their autonomy. However, due to the dynamicity and uncertainty involved in the agents' environments, the enforced norms can sometimes be ineffective. In this paper we propose a runtime supervision mechanism that automatically revises norms when their enforcement appears to be ineffective. The decision to revise norms is taken based on a Bayesian Network that gives information about the likelihood of achieving the overall desirable system objectives by enforcing the norms. Norms can be revised in three ways: relaxation, strengthening and alteration. 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