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Rough Set & Riemannian Covariance Matrix Theory for Mining the Multidimensionality of Artificial Consciousness

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Resource type
Conference Paper
Author/contributor
Title
Rough Set & Riemannian Covariance Matrix Theory for Mining the Multidimensionality of Artificial Consciousness
Abstract
This paper presents a means to analyze the multidimensionality of human consciousness as it interacts with the brain by utilizing Rough Set Theory and Riemannian Covariance Matrices. We mathematically define the infantile state of a robot's operating system running artificial consciousness, which operates mutually exclusively to the operating system for its AI and locomotor functions.
Proceedings Title
Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics
Conference Name
WIMS 2020: The 10th International Conference on Web Intelligence, Mining and Semantics
Publisher
ACM
Place
Biarritz France
Date
2020-06-30
Pages
248-251
DOI
10.1145/3405962.3405974
ISBN
978-1-4503-7542-9
URL
https://dl.acm.org/doi/10.1145/3405962.3405974
Accessed
3/7/25, 7:06 AM
Language
en
Library Catalog
DOI.org (Crossref)
Citation
Lewis, R. (2020). Rough Set & Riemannian Covariance Matrix Theory for Mining the Multidimensionality of Artificial Consciousness. Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics, 248–251. https://doi.org/10.1145/3405962.3405974
Link to this record
http://127.0.0.1:5000/bibliography/7ULPIRUP