{"type":"ScholarlyArticle","title":"Robust Norm Emergence by Revealing and Reasoning about Context: Socially Intelligent Agents for Enhancing Privacy","authors":[{"name":"Nirav Ajmeri","slug":"nirav-ajmeri","given":"Nirav","family":"Ajmeri","affiliation":"† Department of Computer Science , North Carolina State University , Raleigh , NC 27695 , USA"},{"name":"Hui Guo","slug":"hui-guo","given":"Hui","family":"Guo","affiliation":"† Department of Computer Science , North Carolina State University , Raleigh , NC 27695 , USA"},{"name":"Pradeep K. Murukannaiah","slug":"pradeep-k-murukannaiah","given":"Pradeep K.","family":"Murukannaiah","affiliation":"‡ Department of Software Engineering , Rochester Institute of Technology , Rochester , NY 14623 , USA"},{"name":"Munindar P. Singh","slug":"munindar-p-singh","given":"Munindar P.","family":"Singh","affiliation":"† Department of Computer Science , North Carolina State University , Raleigh , NC 27695 , USA"}],"abstract":"Norms describe the social architecture of a society and govern the interactions of its member agents. It may be appropriate for an agent to deviate from a norm; the deviation being indicative of a specialized norm applying under a specific context. Existing approaches for norm emergence assume simplified interactions wherein deviations are negatively sanctioned. We investigate via simulation the benefits of enriched interactions where deviating agents share selected elements of their contexts. We find that as a result (1) the norms are learned better with fewer sanctions, indicating improved social cohesion; and (2) the agents are better able to satisfy their individual goals. These results are robust under societies of varying sizes and characteristics reflecting pragmatic, considerate, and selfish agents.","doi":"10.24963/ijcai.2018/4","url":"https://ijcai.org/proceedings/2018/4","pdf":"https://ijcai.org/proceedings/2018/0004.pdf","markdown":"https://ijcai.org/proceedings/2018/4.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/4","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":4,"pages":{"first":28,"last":34},"keywords":["Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence","Agent-based and Multi-agent Systems: Normative systems"],"videos":[],"references":[{"position":1,"raw":"[Ajmeri et al., 2016] Nirav Ajmeri, Jiaming Jiang, Rada Chirkova, Jon Doyle, and Munindar P. 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