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dc.contributor.authorYuan, X.-
dc.contributor.authorLiebelt, M.J.-
dc.contributor.authorShi, P.-
dc.contributor.authorPhillips, B.J.-
dc.identifier.citationProceedings / International Conference on Machine Learning and Cybernetics. International Conference on Machine Learning and Cybernetics, 2019, vol.2019-July, pp.1-6-
dc.description.abstractAssociation Rules Mining is an approach to discover rules from data sets, and it can establish relationships among elements in a data set. Our research is focused on rule-based agents with Artificial General Intelligence (AGI), which are developed based on the overall environment to achieve functions with cognition. In this paper, we use a modified Association Rules Mining method to find out characteristic rules from data recorded in the training of customized parking scenarios. Fuzzy symbolic elements are recorded during training, and Association Rule Mining selects rules for the AI agent. Experiments have been conducted in a virtual environment to demonstrate the effectiveness of the proposed new algorithm.-
dc.description.statementofresponsibilityXin Yuan, Michael John Liebelt, Peng Shl, Braden J. Phillips-
dc.relation.ispartofseriesProceedings. International Conference on Machine Learning and Cybernetics (ICMLC)-
dc.rights© 2019 IEEE-
dc.subjectProduction rule-based systems; Association rules mining; Artificial general intelligence; Autonomous parking-
dc.titleDevelopment of rule-based agents for autonomous parking systems by association rules mining-
dc.typeConference paper-
dc.contributor.conferenceInternational Conference on Machine Learning and Cybernetics (ICMLC) (7 Jul 2019 - 10 Jul 2019 : Kobe, Japan)-
dc.publisher.placePiscataway, NJ-
dc.relation.grant 170102644-
dc.identifier.orcidYuan, X. [0000-0001-5056-171X]-
dc.identifier.orcidLiebelt, M.J. [0000-0001-6610-2876]-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
dc.identifier.orcidPhillips, B.J. [0000-0001-8288-4791]-
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Electrical and Electronic Engineering publications

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