Query based learning in multi-agent systems

dc.authorid0000-0002-8706-0801
dc.contributor.authorSencer, Safiye
dc.date.accessioned2021-06-23T18:56:34Z
dc.date.available2021-06-23T18:56:34Z
dc.date.issued2011
dc.departmentBAİBÜ, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümüen_US
dc.description2011 International Conference on Artificial Intelligence, ICAI 2011 -- 18 July 2011 through 21 July 2011 -- Las Vegas, NV -- 92464en_US
dc.description.abstractThis study focuses on query based learning in multi-agent systems which include both data management operations and coordination activities. The study is oriented on agent based and database systems with model driven approach (MDA) which provides arrangement of data within a multi-agent system by letting filter with query based learning which supports the decision mechanism within the system. It uses the similarity measure on maximum entropy approach, which is often to find out interesting and meaningful patterns from databases. At the same time, it may generate a variety of rules, such as classification rules, throughout to learning rules of the query based learning process.en_US
dc.identifier.endpage39en_US
dc.identifier.isbn9781601321855
dc.identifier.scopus2-s2.0-84866068452en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage33en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12491/5031
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84866068452&partnerID=40&md5=850f3f92e0009093b3d4b5f6cbad0f2c
dc.identifier.volume1en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSencer, Safiye
dc.language.isoenen_US
dc.relation.ispartofProceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEntropyen_US
dc.subjectModel Driven Approachen_US
dc.subjectMulti-agent Systemen_US
dc.subjectQuery Based Learningen_US
dc.titleQuery based learning in multi-agent systemsen_US
dc.typeConference Objecten_US

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