Concept of A.I. Based Knowledge Generator

Abstract

An important feature of the currently used artificial intelligence systems is their anthropomorphism. The tool of inductive empirical systems is a neural network that simulates the human brain and operates in the "black box" mode. Deductive analytical systems for representation of knowledge use transparent formalized models and algorithms, for example, algorithms of logical inference. They solve many intellectual problems, the solution of which can do without a "deep" anthropomorphic AI. On the other hand, the solution of these problems leads to the formation of alternative artificial intelligence systems. We propose the formation of artificial intelligence systems based on the following principles: exclusion of black box technologies; domination of data conversion systems: the use of direct mathematical modeling. The base of the system is a simulator-a module that simulates a given object. The ontological module selectively extracts structured sets of functional links from the simulator and fills them with corresponding data sets. The final (custom) representation of knowledge is carried out with the help of special interfaces. The concept of simulation-ontological artificial intelligence, based on the principles outlined above, is implemented in the form of parametric analysis in the configuration space and forms the methodological basis of the AI-platform for e-learning.

Triangle Generator for Online Mathematical E-learning

“…Conventional simulation generation (Rotkin, Yavich, & Malev, 2018) is based on direct configuration dependencies exclusively on the initial parameters, which limits the variety of tasks.…”

“…), all of them come down, to one degree or another, not to create really new training materials, but to process existing content stored in databases , or to duplicate task options from templates. To get out of this vicious circle of repeatedly repeating modification of traditional content, a methodology is proposed for generating content based on subject-based simulation models of educational material (Rotkin, Yavich, & Malev, 2018) which allows to switch from processing data from databases knowledge directly to the generation of knowledge in the form of educational content.…”

Configurable Intelligent Design Based on Hierarchical Imitation Models

“…This provides a solid foundation for the development of deterministic AI educational systems. The positive reputation of online educational resources, such as [89,91], can also be attributed to their utilization of educational materials directly aligned with US educational standards, which serve as a reference for standards in many states and regions. It is worth mentioning that government bodies are showing interest in supporting the creation of AI education systems, as evidenced by their funding of relevant projects and their implementation in schools and universities [68,91].…”

“…The positive reputation of online educational resources, such as [89,91], can also be attributed to their utilization of educational materials directly aligned with US educational standards, which serve as a reference for standards in many states and regions. It is worth mentioning that government bodies are showing interest in supporting the creation of AI education systems, as evidenced by their funding of relevant projects and their implementation in schools and universities [68,91]. For instance, the development of an automated educational content generation system [68] resulted in the creation of a new university course on theoretical mechanics, complete with next generation educational materials.…”

Generation of Training Initial Generation Content

“…However, higher demands are placed on teachers, and special consulting groups are being formed for their preparation. The extension of the content generation methodology, with the inclusion of not only educational but also a wide range of research tasks, leads to the concept of an intelligent knowledge generator [19].…”