This scientific article is devoted to the study of complexity theory for solving artificial intelligence (AI) problems and the classification of problems according to NP completeness. is similar to NP-complexity generation methods for boundary programming problems. It is wiser and more efficient to look for sufficiently accurate prediction algorithms than to find evidence that the problem in question belongs to the NP class and therefore wastes time. This scientific article provides a typical example that illustrates all algorithms.
Gary M., Johnson D. "Computing machines and intractable problems". M .: Mir 1982-466 pp.
Ivanova A.P. “Introduction to Applied Programming. Models and Computational Algorithms”. M .: Fizmatlit 2002
Perepelitsa V.A. “Asymptotic approach and solution of some extremal problems on graphs. Problems of Cybernetics "M .: Nauka, 1973
A.V. Yakovlev, A.A. Bezbogov, V.V. Rodin, V. N. Shamkin, CRYPTOGRAPHIC PROTECTION OF INFORMATION
Eremeev A.V., Romanova A.A., Servakh V.V., Chaukhan S.S. Approximate solution of one problem of supply management // Diskretn. analysis and issled. operations. Series 2. 2006. T. 13, No. 1. P. 27–39.
Кодиров Э. С. У., Халилов З. Ш. ВЗАИМОСВЯЗИ И РАЗЛИЧИЯ МЕЖДУ “DEEP LEARNING” И “MACHINE LEARNING” //Universum: технические науки. – 2020. – №. 7-1 (76).
Кодиров Э. С. У., Халилов З. Ш. ВОЗМОЖНОСТИ И ПРЕИМУЩЕСТВА ИСКУССТВЕННОГО ИНТЕЛЛЕКТА (ИИ) И ЛОГИЧЕСКИХ ВЫЧИСЛЕНИЙ //Universum: технические науки. – 2020. – №. 6-1 (75).
Karimov U. et al. USING NEW INFORMATION TECHNOLOGIES IN DISTANCE LEARNING SYSTEM //НОВАЯ ПРОМЫШЛЕННАЯ РЕВОЛЮЦИЯ В ЗЕРКАЛЕ СОВРЕМЕННОЙ НАУКИ. – 2018. – С. 9-11.
R.Khamdamov, U.Begimkulov, N.Taylokov. Information technology in education. Tashkent, 2010.