Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126911
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Type: Journal article
Title: Creating rule-based agents for artificial general intelligence using association rules mining
Author: Yuan, X.
Liebelt, M.
Shi, P.
Phillips, B.
Citation: International Journal of Machine Learning and Cybernetics, 2020; OnlinePubl:1-8
Publisher: Springer
Issue Date: 2020
ISSN: 1868-8071
1868-808X
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Responsibility: 
Xin Yuan, Michael John Liebelt, Peng Shi and Braden J. Phillips
Abstract: In this paper, our focus is on using a rule-based approach to develop agents with artificial general intelligence. In rule-based systems, developing effective rules is a huge challenge, and coding rules for agents requires a large amount of manual work. Association rules mining (ARM) can be used for discovering specific rules from data sets and determining relationships between data sets. In this paper, we introduce a modified ARM method and use it to discover rules that analyse the surrounding environment and determine movements for an agent-guided vehicle that has been designed to achieve autonomous parking. The rules are created by our ARM-based method from training data gained during manual training in customised parking scenarios. In this system, data are represented in terms of fuzzy symbolic elements. We have tested our system by simulation in a virtual environment to demonstrate the effectiveness of this new approach.
Description: Published: 10 July 2020
Rights: © 2020, Springer Nature.
RMID: 1000023857
DOI: 10.1007/s13042-020-01166-8
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Computer Science publications

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