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https://hdl.handle.net/2440/124966
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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 9781728128177 |
ISSN: | 2160-133X 2160-1348 |
Conference Name: | International Conference on Machine Learning and Cybernetics (ICMLC) (7 Jul 2019 - 10 Jul 2019 : Kobe, Japan) |
Statement of Responsibility: | 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: | http://purl.org/au-research/grants/arc/DP 170102644 |
Published version: | https://ieeexplore.ieee.org/xpl/conhome/8942645/proceeding |
Appears in Collections: | Aurora harvest 8 Electrical and Electronic Engineering publications |
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