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

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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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Notes

  1. The rover was driven manually and so did not follow exactly the same path in each run. The number of WMEs generated would vary with minor variations in the travelled path.

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Acknowledgements

This work was partially supported by the Australian Research Council (DP 170102644).

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Correspondence to Xin Yuan.

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Yuan, X., Liebelt, M.J., Shi, P. et al. Creating rule-based agents for artificial general intelligence using association rules mining. Int. J. Mach. Learn. & Cyber. 12, 223–230 (2021). https://doi.org/10.1007/s13042-020-01166-8

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  • DOI: https://doi.org/10.1007/s13042-020-01166-8

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