Concept of A.I. Based Knowledge Generator


Vladimir Rotkin, Roman Yavich, Sergey Malev

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.

Keywords


Artificial intelligence, Knowledge generator, Electronic education, Analytical systems, Simulation modeling, Random generator, Anthology.

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About this article

Title

Concept of A.I. Based Knowledge Generator

Keywords

Artificial intelligence, Knowledge generator, Electronic education, Analytical systems, Simulation modeling, Random generator, Anthology.

DOI

10.20448/journal.509.2018.54.235.241

Date

2018-11-29

Additional Links

Manuscript Submission

Journal

Journal of Education and e-Learning Research
Vol 5, No 4 (2018) Page: 235-241

Print ISSN

2518-0169

Online ISSN

2410-9991

Statistics

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Authors & Affiliations

Vladimir Rotkin
Ariel Scientific Innovations (ASI) Ltd, Ariel
Israel

Roman Yavich
Ariel University, Ariel
Israel

Sergey Malev
Ariel University, Ariel
Israel


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Journal of Education and e-Learning Research

Online ISSN: 2410-9991  |  Print ISSN: 2518-0169

 

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