=Paper=
{{Paper
|id=Vol-3041/402-406-paper-74
|storemode=property
|title=Imperfect Knowledge Base Self-Organization in Robotic Intelligent Cognitive Control: Quantum Supremacy on Big Data Analysis
|pdfUrl=https://ceur-ws.org/Vol-3041/402-406-paper-74.pdf
|volume=Vol-3041
|authors=Sergey Ulyanov,Andrey Shevchenko,Allashevchenko,Andrey Reshetnikov
}}
==Imperfect Knowledge Base Self-Organization in Robotic Intelligent Cognitive Control: Quantum Supremacy on Big Data Analysis==
https://ceur-ws.org/Vol-3041/402-406-paper-74.pdf
Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and
Education" (GRID'2021), Dubna, Russia, July 5-9, 2021
IMPERFECT KNOWLEDGE BASE SELF-ORGANIZATION
IN ROBOTIC INTELLIGENT COGNITIVE CONTROL:
QUANTUM SUPREMACY ON BIG DATA ANALYSIS
S.V. Ulyanov1,2, A.V. Shevchenko 2, A.A. Shevchenko 2,
A.G. Reshetnikov1,2,3,a
1
Joint Institute for Nuclear Research – Laboratory of Information Technology, Dubna, 6 Joliot-Curie
St, 141980, Russia
2
Dubna State University – Institute of System Analysis and Control, Dubna, 19 Universitetskaya St,
141982, Russia
3
Plekhanov Russian University of Economics, Laboratory of cloud technologies and Big Data
analytics; Moscow, Stremyanny Lane, 36, 117997, Russia
E-mail:aagreshetnikov@mail.ru
The smart control design with secured achievement of information-thermodynamic trade-off
interrelations is main goal for quantum self-organization algorithm of imperfect KB. Quantum genetic
algorithm applied on line for the quantum correlation’s type searching between unknown solutions in
quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft
computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations
between main control quality measures (as new design laws) discussed. Sophisticated synergetic
quantum information effect in autonomous robot in unpredicted control situations) and swarm robots
with imperfect KB exchanging between “master – slaves” introduced.A new robust smart controller on
line designed from responses on unpredicted control situations of any imperfect KB applying quantum
hidden information extracted from quantum correlation discussed. Within the toolkit of classical
intelligent control, the achievement of the similar synergetic information effect is impossible.
Keywords: quantum genetic algorithm, intelligent cognitive robotics, quantum inference
Sergey Ulyanov, Andrey Shevchenko, AllaShevchenko, Andrey Reshetnikov
Copyright © 2021 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and
Education" (GRID'2021), Dubna, Russia, July 5-9, 2021
1. Introduction
According to the definition of modern control problems, the achievement of required
robustness property for a complex ill-defined control object models is possible with applying the
computational intelligence toolkit. The goal of this article is the description of the applied aspects of
developed intelligent design technology of robust knowledge bases (KB) [1-7] using the information
synergy effects of quantum knowledge self-organization [2] in unpredicted and risky control
conditions [5].
Four statements from quantum information theory and quantum thermodynamics are applied
in this developed approach: 1) minimum entropy production rate principle of the system “control
object + intelligent controller” that quadrantes the achievement of control goal with minimum of work
waste in control object and in intelligent controller; 2) minimum information entropy principle for
design intelligent cognitive controller that required minimum of initial information for intelligent
controller action; 3) the amount of the work wasted on the extraction of hidden quantum information is
less than the amount of work done on the received extracted quantum hidden information; and 4) the
solution problem search of maximum extractable value work identical to a search of the minimum
wasted entropy done on this work extraction[8-9]. The article task is the description of the IT-design
process a robust sophisticated KB of intelligent cognitive controller that produce control force that
satisfied to these requirements.
2. Imperfect KB quantum self-organization process
The role of specific quantum hidden information effects for smart control design described in
[2]. The amount of hidden quantum information [2-4, 6] extracted from control classical states
considered as the additional information-thermodynamic control force source.
In systems inspired by nature, robustness is determined by the natural process of self-
organization [1-2]. The process of quantum self-organization of KBs, in which the robustness property
is achieved, is shown in Fig. 1. Natural evolution consists of the following stages: 1) creating a
template; 2) self-assembling; 3) self-organization (see level 2 in Fig. 1).
As is known from the theory of quantum computing, each quantum algorithm contains such
unitary quantum operators as interference, superposition, entanglement (quantum oracle) and
measurement classical operator (irreversible and used for measurement of quantum computations).
The quantum fuzzy inference (QFI) model is based on the corresponding quantum operators and
accumulates the principles of self-organization. A quantum self-organization control algorithm, based
on QFI, is shown in Fig. 1.
Figure 1. Quantum search hierarchical structure of self-organization robust KB’s design system
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Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and
Education" (GRID'2021), Dubna, Russia, July 5-9, 2021
Thus, this quantum control algorithm uses the basic principles of quantum information theory,
which are useful information resources of QFI. Based on these principles, it is possible to extract
additional hidden information and eliminate the redundancy of information for the formation of a
control action, thereby increasing the stability of the ICS, providing robustness and accuracy of control
in conditions of uncertainty or poorly formalized description of the external environment.
Consequently, the inclusion of stability in the architecture of the structure of an intelligent control
system contributes to an increase in its efficiency.
3. Benchmark’s simulation of smart control with QFI
In Figure 2 shows the results of an experiment of control in unexpected situations for an object
"cart-double pole" and a 7 degrees of freedomredundant manipulator. The experiment compares the
different controllers: PID controller, two fuzzy controllers (FC1, FC2) and three QFI controllers based
on different types of correlations: Quantum-Time (Q-T), Quantum-Space (Q-S), Quantum-SpaceTime
(Q-ST).
In the simulation and experiment, the structure of a robust ICS based on QFI (see Fig. 2) and
QAG (see Figure 3) was used. Based on the training signal taken directly from the control object,
using the QCOptKB™ software toolkit, a KB of FC was designed. An abnormal situation was
simulated by a threefold delay in the feedback sensor signal.
Figure 2. The experiment of control in unexpected situations for an object "cart-double
pole" and a 7 degrees of freedomredundantmanipulator
The experimental results show that the accuracy of a quantum controller is more than 10,000
(see Fig. 2, right side) times higher than that of a controller based on soft computing. Under conditions
of uncertainty, the controller based on soft computing dramatically increases the control error, thereby
failing to achieve the control goal (see Table 1). Comparison of controllers shows the presence of a
synergistic effect of self-organization in the design of robust KBs based on imperfect KBs of FCs. The
control coefficients of the PID controller are based on the feedback of imperfect KB (see the “QFI
block” in Fig. 1), forming a control action in online. This is achieved by extracting an additional
information resource using QFI in the form of quantum information hidden in the classical states of
the control action as a new control error of the output signal of an imperfect KB [1-2].
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Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and
Education" (GRID'2021), Dubna, Russia, July 5-9, 2021
Table 11. Comparison of the different regulators
Cart motion, cm
Time, sec
PID FC1 FC2 QFI (Q-S) QFI (Q-ST) QFI (Q-T)
1 -1 -1 -1 1 -1 -1
2 5 3 5 5 3 4
3 -35 -4 -26 -4 -2 -3
4 60 5 36 6 4 5
5 - -5 -60 -5 -4 -7
6 - 10 - 5 8 6
7 - -14 - -4 -6 -9
8 - 23 - 4 5 7
9 - -32 - -6 -8 -3
10 - 50 - 9 6 4
11 - - - -9 -4 -7
80
Choice probability
60
40
20
0
0 1000 2000 3000 4000 5000 6000
Generation
Q-S-T Q-T Q-S
Figure 3. The result of the QGA
However, after 200 generations the probability of spatio-temporal correlations decreases to
60% (see Fig. 4).
Q-S; 16
Q-T; Q-S-T;
24 60
Figure 4. The probability of spatio-temporal correlations after 200 generations
The described method is differed from others results described in [7-8].
4. Conclusion
This paper describes a method for designing intelligent control systems using a quantum
algorithmic gate for quantum fuzzy inference based on a quantum genetic algorithm. This method in
online allows to achieve global sustainability in the face of unforeseen management situations.
Building on the computing power of classical computers, new types of quantum computational
intelligence tools such as quantum and soft computing are used. The presented QFI model implements
a new type of quantum search algorithm with the introduction of a quantum genetic algorithm, which
makes it possible to design a robust ICS with classical nonlinear objects (such objects can be
considered as a standard for testing the effectiveness of an ICS) control in conditions of global
instability [4, 6-8, 12] and can be used for big data analysis. Such a synergistic effect is achieved using
hidden quantum information (as an additional resource), which obeys only the laws of quantum
physics and has no analogues in classical physics.
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Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and
Education" (GRID'2021), Dubna, Russia, July 5-9, 2021
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