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Type: Conference paper
Title: Development of rule-based agents for autonomous parking systems by association rules mining
Author: Yuan, X.
Liebelt, M.J.
Shi, P.
Phillips, B.J.
Citation: Proceedings / International Conference on Machine Learning and Cybernetics. International Conference on Machine Learning and Cybernetics, 2019, vol.2019-July, pp.1-6
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2019
Series/Report no.: Proceedings. International Conference on Machine Learning and Cybernetics (ICMLC)
ISBN: 172812817X
ISSN: 2160-133X
Conference Name: International Conference on Machine Learning and Cybernetics (ICMLC) (7 Jul 2019 - 10 Jul 2019 : Kobe, Japan)
Statement of
Xin Yuan, Michael John Liebelt, Peng Shl, Braden J. Phillips
Abstract: Association 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.
Keywords: Production rule-based systems; Association rules mining; Artificial general intelligence; Autonomous parking
Rights: © 2019 IEEE
DOI: 10.1109/ICMLC48188.2019.8949201
Grant ID: 170102644
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Appears in Collections:Aurora harvest 8
Electrical and Electronic Engineering publications

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