This article presents the development of automatic clone detection for verification students programs - the Detector of Clones. The Levenshtein Distance is used for the initial assessment of the similarity of the two token sequences which is calculated by the Wagner-Fisher algorithm. Verification of two programs is carried out by the fingerprint method and by the winnowing method. To improve the results of plagiarism assessment, an additional calculation was added to the implementation of the winnowing method: the detector compares not only the labels themselves, but also the sequences near the matching labels. The detector of clones is formed for the cycle of disciplines Object Oriented Programming, Object Oriented Analysis and Design on the Moodle platform. These tools support C-like languages (C++ and C#). In general, the work of the detector undoubtedly made it possible to increase the consistency in verification of student programs.

One of the promising areas for using Prolog-systems is to solve logical tasks. This study outlines a solution approach based on the state generation procedure and the verification procedure. A solution to a logical task is presented, which demonstrates in practice the proposed approach and method of specifying a procedure for generating states. In the proposed example, a bit chain is generated that defines the code of a particular letter in the solution of the applied problem. Building a solution by means of code generation with verification allows not storing in the knowledge base a binary tree of all possible codes. The process of generating new states can be associated with the training of the program, with the dynamic formation of the knowledge base. The approach is based on the capabilities of software environments for adding facts and rules to existing ones, which were obtained as the results of the program or its stages. In this case, the entire program is the generating rule. An analysis of the constructed and tested procedures for the dynamic generation of states and the generation of facts allows us to talk about the applicability of such a solution for certain applied problems.

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The study explores the problems of reinforcement learning and finding non-obvious play strategies using reinforcement learning. Two approaches to agent training (blind and pattern-based) are considered and implemented. The advantage of the self-learning approach with reinforcement using patterns as applied to a specific game (tic-tac-toe five in a row) is shown. Recorded and analyzed the use of unusual strategies by an agent using a pattern-based approach.