Artificial Intelligence for Complexity Theory



artificial intelligence, parsing, algorithm


In this continued series of work, we present the theoretical and practical results towards reasoning with modern methods of Artificial Intelligence (AI). We justify our methodology with help of illustrative examples from Computer Science relying on the regular expression matching algorithm and application of the proposed solution for the task of identifying files consistency according to the unknown format. We will also give several notable proofs to the classical theorems which in some sense are coherent to the terms like AI and algorithmic complexity, however, or at least, nowadays they’re solved involving the huge amount of hardware resources and together constitute the new formation in the modern age with help of specifically crafter hardware modules – we’re still about to represent the model in more classical understanding from the point of view of computational complexity, concise reasoning and computer logic within the classical models, theorems and proofs as the base approach of estimating the costs needed to build Artificial Neural Networks (ANN) or Machine Learning (ML) data.


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Author Biography

Mirzakhmet Syzdykov, Satbayev University

Born 11/09/84. 2006-2009, aspirant at Institute of Problems in Informatics and Control




How to Cite

Syzdykov, M. (2023). Artificial Intelligence for Complexity Theory. ADVANCED TECHNOLOGIES AND COMPUTER SCIENCE, 1(3), 27–31. Retrieved from



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