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International Journal of Machine Learning and Cybernetics

Erschienen in:

10.07.2020 | Original Article

Creating rule-based agents for artificial general intelligence using association rules mining

verfasst von: Xin Yuan, Michael John Liebelt, Peng Shi, Braden J. Phillips

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2021

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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.

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Titel: Creating rule-based agents for artificial general intelligence using association rules mining
verfasst von: Xin Yuan
Michael John Liebelt
Peng Shi
Braden J. Phillips
Publikationsdatum: 10.07.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Machine Learning and Cybernetics / Ausgabe 1/2021
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-020-01166-8

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