Please use this identifier to cite or link to this item: http://dspace2020.uniten.edu.my:8080/handle/123456789/3966
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dc.contributor.authorHammoud, M.en_US
dc.contributor.authorTang, A.Y.C.en_US
dc.contributor.authorAhmad, A.en_US
dc.date.accessioned2017-11-01T05:56:38Z-
dc.date.available2017-11-01T05:56:38Z-
dc.date.issued2016-
dc.identifier.urihttps://pure.uniten.edu.my/en/persons/azhana-ahmad/publications/-
dc.description.abstractSocial norms main objective is to regulate autonomous agents' behaviour in an open normative multi-agent system. Norms in these societies are dynamically created and disappeared according to the society's needs. Consequently, norms effects on agents or on the environment are not observable at the moment of creation. Norms practicing consequences might be either positive, like increasing the educational level of a society by conducting social discussions. Or negative, like causing money loss in gambling. Or the norm might have neutral consequences. In this paper, we propose a technique to detect negative norms in an open normative multi-agent system. Our technique has two main stages: i) Observation and ii) Analysis. The observation stage relies on the overhearing approach of monitoring where the messages that are exchanged between agents are observable. All observations are then analysed in order to detect negative norms. Negativity of a norm is based on its effect on agents or on the environment. In this technique, we adopted ATN concept to represent norms. This technique is implemented using Java and JADE. Testing results of this technique shows that it works properly, and detects negative norms according to the defined negativity threshold. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.en_US
dc.language.isoenen_US
dc.subjectMulti agent systemsen_US
dc.titleNegative norms detection technique in open normative multi-agent systemsen_US
dc.typeConference Paperen_US
dc.relation.conferenceICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence Volume 2, 2016, Pages 241-249en_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.openairetypeConference Paper-
Appears in Collections:CCI Scholarly Publication
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