Brain Principles Programming

The monograph “Strong Artificial Intelligence. On the Approaches to Superintelligence,” referenced by this paper, provides a cross-disciplinary review of Artificial General Intelligence (AGI). As an anthropomorphic direction of research, it considers Brain Principles Programming (BPP)—the formalizat...

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Bibliographic Details
Published in:Doklady. Mathematics 2022-12, Vol.106 (Suppl 1), p.S101-S112
Main Authors: Vityaev, E., Kolonin, A., Kurpatov, A., Molchanov, A.
Format: Article
Language:English
Subjects:
Advanced Studies in Artificial Intelligence and Machine Learning
Algorithms
Artificial intelligence
Brain
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Principles
Citations: Items that this one cites
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Summary:The monograph “Strong Artificial Intelligence. On the Approaches to Superintelligence,” referenced by this paper, provides a cross-disciplinary review of Artificial General Intelligence (AGI). As an anthropomorphic direction of research, it considers Brain Principles Programming (BPP)—the formalization of universal mechanisms (principles) of the brain’s work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed by us earlier that modeling cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K. Anokhin’s Theory of Functional Brain Systems, Eleonor Rosch’s prototypical categorization theory, Bob Rehder’s theory of causal models and “natural” classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithms operation.
ISSN:1064-5624
1531-8362
DOI:10.1134/S1064562422060217