Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Development of rule-based agents for autonomous parking systems by association rules mining|
|Citation:||Proceedings of the 19th International Conference on Machine Learning and Cybernetics (ICMLC 2019), 2019 / vol.2019-July, pp.1-6|
|Publisher Place:||Piscataway, NJ|
|Series/Report no.:||Proceedings. International Conference on Machine Learning and Cybernetics (ICMLC)|
|Conference Name:||International Conference on Machine Learning and Cybernetics (ICMLC) (07 Jul 2019 - 10 Jul 2019 : Kobe, Japan)|
|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|
|Grant ID:||http://purl.org/au-research/grants/arc/DP 170102644|
|Appears in Collections:||Electrical and Electronic Engineering publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.