Rough Set & Riemannian Covariance Matrix Theory for Mining the Multidimensionality of Artificial Consciousness
Resource type
Conference Paper
Author/contributor
- Lewis, Rory (Author)
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.
Date
2020-06-30
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
Place
Biarritz France
Publisher
ACM
Pages
248-251
Language
en
ISBN
978-1-4503-7542-9
Accessed
3/7/25, 7:06 AM
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
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