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01-04-2023 | INTELLIGENT SYSTEMS

Control Systems: Intellectualization As a Response to Challenges of the Big Data Era

Author: M. I. Zabezhailo

Published in: Automatic Documentation and Mathematical Linguistics | Issue 2/2023

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This article analyzes the problem of processing massive amounts of data under strict time constraints in control systems. One strategy for solving this problem involves the use of artificial intelligence (AI) technologies. The range of mathematical models traditionally used in control systems has been supplemented by AI-based solutions that involve computer-oriented formalizations of strategies used by human experts to solve problems of this type: so-called interpolation/extrapolation (I/E) models. This article discusses certain significant features of I/E-type solutions, in particular, their ability to generate effective solutions in open big data environments (where the behavior of the control object is not characterized by a single NORMAL state), the problem of identifying stable (inheritable) solutions in the set of permissible solutions when updating empirical data on behaviors of the control object, and finally the problem of identifying empirical dependencies of a causal nature in the current data to compile informal interpretations of alternatives (recommendations) generated by the digital control system to be presented to decision makers (DMs).

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For example, control of nuclear power plants involves hundreds or more parameters of control object functioning being displayed just on the main control panel; the totality of all possible combinations of these parameter values is practically immeasurable. Consequently, it is simply impossible to “go through” all corresponding states of the control object to identify associated risks and select effective means of managing those risks.

In problems of control of large technological systems, this relation is in a number of cases only partially defined—determined by examples (control actions adequate to the current situation) and counterexamples (incorrect decisions)—and requires additional definition in each situation that is new for the control object.

For example, the absence of cycles in sequentially applied rule chains.

See, for example, the problem of identification of cycles in computer networks using routing schemes with multiple switches.

At least in the given current situation.

When the situation is permissible.

When the situation is assessed as non-permissible.

For example, in a situation when malware got inside the perimeter of the network and began to act, but any scenario involving disconnecting the network and then restoring it from a back-up configuration is excluded due to the prohibition of such a decision for some external business reasons.

The term was introduced by Academician Yu.I. Zhuravlev.

For example, machine learning artifacts—effects of overfitting etc.

Preferably, also efficient (not requiring exponentially complex calculations), so that, for example, the DM’s situational awareness can be kept continuously up to date.

Preferably, efficient (for example, implemented by polynomial complex algorithms)

Or, in the conditions of the open data effect—situations when existing data may potentially be updated with information of a fundamentally new and previously unknown nature, making it possible for multiple states to be classified as NORMAL.

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Title: Control Systems: Intellectualization As a Response to Challenges of the Big Data Era
Author: M. I. Zabezhailo
Publication date: 01-04-2023
Publisher: Pleiades Publishing
Published in: Automatic Documentation and Mathematical Linguistics / Issue 2/2023
Print ISSN: 0005-1055
Electronic ISSN: 1934-8371
DOI: https://doi.org/10.3103/S0005105523020048

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Control Systems: Intellectualization As a Response to Challenges of the Big Data Era

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