Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum / Soft Computing Approach

Authors

  • Alla A Mamaeva Dubna State University, Russia INESYS LLC (EFKO GROUP), Russia
  • Andrey V Shevchenko Dubna State University, Russia INESYS LLC (EFKO GROUP), Russia
  • Sergey Victorovich Ulyanov State University “Dubna”

DOI:

https://doi.org/10.30564/aia.v2i1.1323

Abstract

The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular, case, the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated. 

Keywords:

neural interface； computational intelligence toolkit； intelligent control system； deep machine learning； emotions； quantum soft computing optimizer

References

[1] Petrov, B, Ulanov, G., Ulyanov S. and Hazen E. Information semantic problems in organization control [M]. Nauka, 1977, pp. 452.

[2] Ozer, E. and Feng, M. Structural reliability estimation with participatory sensing and mobile cyber-physical structural health monitoring systems [J]. Appl. Sci. 9, 2019, pp. 2840.

[3] Noor A. Potential of cognitive computing and cognitive systems [J]. Open Eng, 5, 2015, pp. 75-88.

[4] Chie H., Takato H. and Takayuki N. Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks [J]. CoRR, 2018, available at: http://arxiv.org/abs/1808.08447.

[5] Rozaliev V. Postroenie matematicheskoj modeli emocij: Integrirovannye modeli i myagkie vychisleniya v iskusstvennom intellekte [R]. V Mezhdunarodnaya nauchno-prakticheskaya konferenciya "Integrirovannye modeli i myagkie vychisleniya v iskusstvennom intellekte" Sbornik nauchnyh trudov, 2009, pp. 950-957.

[6] Bazgir O., Mohammadi Z. and Habibi S. Emotion recognition with machine learning using EEG signals [R]. 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME), 2018, pp. 1-5.

[7] Xie H., Pan J. and Wen Z. An EEG-based brain computer interface for emotion recognition and its application in patients with disorder of consciousness [J]. IEEE Transactions on Affective Computing, 2019, pp. 1.

[8] Ulyanov S. and Litvintceva L. Intelligent robust control: soft computing technology [M]. VNIIgeosistem, 2011, pp. 406.

[9] Ulyanov S.V. (inventor) Self-organizing quantum robust control methods and systems for situations with uncertainty and risk // Patent US 8788450 B2. – Date Publ. July 22, 2014.

[10] Sandberg H. et all. Maximum work extraction and implementation costs for nonequilibrium Maxwell’s demon // Physical Review E. 2014. No 4. pp. 042119.

[11] Ulyanov S., Yamafuji K., Gradetsky V. and Fukuda T. Development of intelligent mobile robots for service use and mobile automation systems including wall climbing robots: Pt. 1. Fundamental design principles and motion models [J]. International Journal of Intelligent Mechatronics: Design and Production. 1, 3, 1997, pp. 111-143.

[12] Sagawa T, Ueda M. Minimal Energy Cost for Thermodynamic Information Processing: Measurement and Information Erasure [J]. Phys. Rev. Lett. 102, 25. pp. 250602.

[13] Horowitz J. M., Sandberg H. Second-law-like inequalities with information and their interpretations [J]. New Journal of Physics. 16, 2014, pp. 125007.

[14] Ulyanov S.V., Litvintseva L.V., Kurawaki I. et al. Principle of minimum entropy production in applied soft computing for advanced intelligent robotics and mechatronics [J]. Soft Computing. 4, 3, 2000, pp. 141–146.

[15] Sieniutycz S., et all. Framework for optimal control in multistage energy systems [J]. Physics Reports, 326, 2, 2000.

[16] Ulyanov S.V. Quantum relativistic informatics. LAP LAMBERT Academic Publishing, OmniScriptum GmbH & Co. KG, 2015.

[17] Sadeghieh A., Roshanian J., Najafari F. Implementation of an intelligent adaptive controller for an electrohydravlic servo system based on a brain mechanism of emotional learning [J]. Intern. J. of Advanced Robotic Systems (INTECH). 9, 2012, pp. 1–12.

[18] E. Daryabeigi, A. Zarchi, Gh. R. Arab Markadeh, M. A. Rahman. Implementation of Emotional Controller (BELBIC) for Synchronous Reluctance Motor Drive Proc [R]. IEEE Intern. Electric Machines & Drivers Conf. (IEMDC), 2011, pp. 1066-1093.

[19] L. V. Litvintseva, I.S. Ulyanov, S. V. Ulyanov and S. S. Ulyanov. Quantum fuzzy inference for knowledge base design in robust intelligent controllers [J]. J. of Computer and Systems Sciences Intern., 46, 9, 2007, pp. 908 – 961.

[20] Ulyanov, S. V Intelligent Robust Control System Based on Quantum KB-Self-organization: Quantum Soft Computing and Kansei / Affective Engineering Technologies [J]. Springer International Publishing, 2014, pp. 37-48.

[21] Takayuki T., Junji O., Litvintseva L., Kazuo Y. and Ulyanov S. Intelligent control of a mobile robot for service use in office buildings and its soft computing algorithms [J]. Journal of Robotics and Mechatronics, 8, 1996, pp. 538-554.

[22] Dawson G. and Toth K. Autism spectrum disorders. In D. Cicchetti & D. J. Cohen (Eds.). [J]. Developmental psychopathology: Risk, disorder, and adaptation, 2006, pp. 317-357.

[23] Stanton C., Kahn P. Jr., Severson R., Ruckert J. and Gill B. Robotic Animals Might Aid in the Social Development of Children with Autism [R]. 3rd ACM/IEEE International Conference on Human-Robot Interaction, 2008.

[24] Wei C., Wenxu S., Xinge L., Sixiao Zh., Ge Zh., Yanting W., Sailing H., Huilin Zh. and Jiajia Ch. Could Interaction with Social Robots Facilitate Joint Attention of Children with Autism Spectrum Disorder? [J]. Computers in Human Behavior, 2019, pp. 98.

[25] Palestra, G., Carolis, B.D., & Esposito, F. Artificial Intelligence for Robot-Assisted Treatment of Autism [J]. WAIAH@AI*IA, 2017.

[26] Cho, S.J. and Ahn, D. Socially Assistive Robotics in Autism Spectrum Disorder [J]. Hanyang Medical Reviews, 36, 2016, pp. 17.

[27] Rudovic O., Lee J., DaiM., Schuller B. and Picard R. W. Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy [J]. Science. 3, 2018.

[28] Ulyanov S., Mamaeva A. and Shevchenko A. Programmnaya realizaciya modulya obrabotki dannyh dlya kognitivno-intellektual'noj sistemy dlya detej-autistov [R]. «Sbornik dokladov XXV Mezhdunarodnoj konferencii «MATEMATIKA. KOMPYUTER. OBRAZOVANIE» 25, 2018, pp. 398.

[29] Ulyanov S., Mamaeva A. and Shevchenko A. Kognitivno-intellektual'naya sistema diagnostiki, obucheniya i adaptacii detej-autistov. Chast. 1 [J]. Sistemnyj analiz v nauke i obrazovanii, 5, 2016, available at:http:/www.sanse.ru/archive/42.

[30] Ulyanov S., Mamaeva A. and Shevchenko A. Kognitivno-intellektual'naya sistema diagnostiki, obucheniya i adaptacii detej-autistov. Chast 2. Opredelenie emocij [J]. Programmnye produkty i sistemy: elektron. nauch. zhurnal. 4, 2017, available at: http://swsys-web.ru/cognitive-intellectual-system-for-diagnosis-and-education-of-autistic-children-2.html.

[31] Nikolaev A. Spektral'nye harakteristiki EEG na pervom etape resheniya razlichnyh prostranstvennyh zadach [J]. Psihologicheskij zhurnal, 15, 6, 1994, pp. 100-106.

[32] Lapshina T. Psihofiziologicheskaya diagnostika emocij cheloveka po pokazatelyam EEG [R]. Materialy Mezhdunarodnoj nauchno-prakticheskoj konferencii "Razvitie nauchnogo naslediya Borisa Mihajlovicha Teplova v otechestvennoj i mirovoj nauke" Nauchnyj sbornik. Moskva: BF "Tverdislov". Moskva, 2006, pp. 160-165.

[33] Fretska E., Bauer H., Leodolter M. and Leodolter U. Loss of control and negative emotions: a cortical slow potential topography study [J]. International Journal of Psychophysiology. 33, 1999, pp. 127-141.

[34] Ulyanov S., Reshetnikov A. and Mamaeva A. Gibridnye kognitivnye nechetkie sistemy upravleniya avtonomnym robotom na osnove nejrointerfejsa i tekhnologii myagkih vychislenij [J]. Programmnye produkty i sistemy / Software & Systems, 30, 3, 2017, pp. 420–424.

[35] Ulyanov S.V., Yamafuji K. Fuzzy intelligent emotion and instinct control of a robotic unicycle [R]. In Proc. 4th Intern. Workshop on Advanced Motion Control. 18-21 Mar., 1996, Mie, Japan, 1, 1996, pp. 127-132.

[36] Ulyanov S.V., Watanabe S., Yamafuji K. A new physical measure for mechanical controllability of a robotic unicycle on basis of intuition, instinct and emotion computing [R]. In Proc. 2nd Intern. Conf. on Application of Fuzzy Systems and Soft Computing. June 25-27, 1996, pp. 78-92.

[37] Ulyanov V.S., Ohkura T., Yamafuji K., Ulyanov S.V. Intelligent control of an extension-less robotic unicycle: A study of mechanical controllability via minimum entropy criteria [J]. Lecture Notes in Control and Information Sciences: Progress in System and Robot Analysis and Control Design, 243, 1999, pp. 559-570.

[38] Hagiwara T., Ulyanov S.V., Takahashi K., Diamante O. An application of a smart control suspension system for a passenger car based on soft computing [R]. Yamaha Motor Technical Report, 2003.01.15.

[39] EU PCT Patent WO 2004/012139 A2 (PCT/US2003/023727), “Intelligent mechatronic control suspension system based on quantum soft computing” (Inventor: S. V. Ulyanov). [P], International publication Date: 5 February 2004 (US Patent US 2004/0024750 A1. Publ. Date: Feb. 5, 2004).

[40] US Patent No 2006,0218 A1, "System for soft computing simulation" (Inventor: S. V. Ulyanov). [P],, Date of patent: Sept. 2006, 2006.

[41] Ulyanov S. Intelligent self-organized robust control design based on quantum / soft computing technologies and Kansei engineering [J]. Computer Science Journal of Moldova, 21, 62, 2013, pp. 242-279.

[42] Ulyanov S.V., Yamafuji K. Intelligent self-organized cognitive controllers. Pt. 1: Kansei / affective engineering and quantum / soft computing technologies [J]. System Analysis in Science and Education, 4, 2014, available at: http:/www.sanse.ru/archive/48.

[43] Kak S. On Quantum Neural Computing [J]. Inf. Sci, 83, 1995 pp 143-160.

[44] Menner T. Quantum Artificial Neural Networks [D]. Univ. of Exeter, UK, 1998.

[45] Gandhi V., Prasad G., Coyle D. et all. Quantum neural network-based EEG filtering for a brain-computer interface [J]. IEEE Trans. on Neural Network and Learning Systems, 25, 2, 2014, pp. 278-288.

[46] Ulyanov S.V., Feng M., Ulyanov V.S., Yamafuji K., Fukuda T., Arai F. Stochastic analysis of time-variant nonlinear dynamic systems. Part 1: the Fokker-Planck-Kolmogorov equation approach in stochastic mechanics [J]. Prob. Engng. Mech., 13, 3, 1998, pp. 183-203.

[47] Elio Conte, Rui Freire Lucas. First Time Demonstration of the Quantum Interference Effect during Integration of Cognition and Emotion in Children [J]. World Journal of Neuroscience, 2015, 5, pp. 91-98.

[48] Diederik Aerts and Sandro Sozzo Quantum Interference in Cognition: Structural Aspects of the Brain [J}. arXiv:1204.4914v1 [cs.AI] 22 Apr 2012

[49] Clark K. Basis for a neuronal version of Grover’s quantum algorithm [J]. Frontiers in Molecular Neuroscience. 2014, DOI:https://doi.org/10.3389/fnmol.2014.0002.http://community.frontiersin.org/people/u/52068

[50] Alexander J. Shackman, Tim V. Salomons, Heleen A. Slagter, Andrew S. Fox, Jameel J. Winter and Richard J. Davidson. The integration of negative affect, pain and cognitive control in the cingulate cortex [J]. Nature, 12, 2011.

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How to Cite

Mamaeva, A. A., Shevchenko, A. V., & Ulyanov, S. V. (2020). Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum / Soft Computing Approach. Artificial Intelligence Advances, 2(1), 1–30. https://doi.org/10.30564/aia.v2i1.1323

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Vol. 2 , Iss. 1 (April 2020)

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Articles

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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