Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

Authors

  • Andrey Ivanovich Kostogryzov Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova Str. 44, bld.2, Moscow, 119333, Russia; Gubkin Russian State University of Oil and Gas (National Research University) Leninsky Av. 65, bld. 1, Moscow, 119991, Russia

DOI:

https://doi.org/10.30564/aia.v1i2.1195

Abstract

The approach for probabilistic rationale of artificial intelligence systems actions is proposed. It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling. The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.  The approach includes description of the proposed probabilistic models, optimization methods for rationale actions and incremental algorithms for solving the problems of  supporting decision-making on the base of monitored  data and rationale a robot actions in uncertainty conditions. The approach means practically a proactive commitment to excellence in uncertainty conditions. A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.

Keywords:

Analysis; Artificial intelligence systems; model; Operation; prediction; Probability; Rationale; Risk; System; System engineering

References

[1] Alan Turing, Computing Machinery and Intelligence, Mind, vol. LIX, 1950, 236: 433—460.

[2] Experimental Robotics. Springer, 2016: 913.

[3] A. Ajoudani, Transferring Human Impedance Regulation Skills to Robots, Springer, 2016: 180.

[4] Geometric and Numerical Foundations of Movements. Springer, 2017: 417.

[5] Robotics Research. Springer, 2017: 646.

[6] Cybernetics Approaches in Intelligent Systems. Computational Methods in Systems and Software, vol. 1, Springer, 2017: 405.

[7] Applied Computational Intelligence and Mathematical Methods. Computational Methods in Systems and Software, 2017, 2: 393.

[8] R. Valencia, J. Andrade-Cetto, Mapping, Planning and Exploration with Pose SLAM, Springer, 2018: 124.

[9] The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue, Springer, 2018: 692.

[10] G. Antonelli, Underwater Robots, Springer, 2018: 374.

[11] Cognitive Reasoning for Compliant Robot Manipulation, Springer, 2019: 190.

[12] G. Venture, J.-P. Laumond, B. Watier Biomechanics of Anthropomorphic Systems, Springer, 2019: 304.

[13] A. Santamaria-Navarro, J. Solà, J. Andrade-Cetto, Visual Guidance of Unmanned Aerial Manipulators, Springer, 2019: 144.

[14] S. Tadokoro, Disaster Robotics, Springer, 2019: 532.

[15] Feller W. An Introduction to Probability Theory and Its Applications. Vol. II, Willy, 1971.

[16] Martin J. System Analysis for Data Transmission. V. II, IBM System Research Institute. Prentice Hall, Inc., Englewood Cliffs, New Jersey, 1972.

[17] Gnedenko B.V. et al. Priority queueing systems, МSU, Moscow, 1973: 448.

[18] Kleinrock L. Queueing systems, V.2: Computer applications, John Wiley & Sons, New York, 1976.

[19] Matweev V.F. & Ushakov V.G. Queuing systems. MSU, Moscow, 1984: 242.

[20] Kostogryzov A.I. Conditions for Efficient Batch Job Processing of Customers in Priority-Driven Computing Systems Where the Queueing Time Is Constrauned, «Avtomatika i telemehanika». 1987, 12: P.158-164.

[21] Kostogryzov A.I. Study of the Efficiency of Combinations of Different Disciplines of the Priority Service of Calls in the Computer Systems, «Kibernetika i sistemny analiz». 1992, 1: 128-137.

[22] Kostogryzov, A.I., Petuhov, A.V. & Scherbina, A.M.. Foundations of evaluation, providing and increasing output information quality for automatized system. Moscow: “Armament. Policy. Conversion”, 1994.

[23] Gnedenko B.V., Korolev V. Yu., Random Summation: Limit Theorems and Applications. – Boca Raton: CRC Press, 1996.

[24] Kostogryzov, A.I. Software Tools Complex for Evaluation of Information Systems Operation Quality (CEISOQ). Proceedings of the 34-th Annual Event of the Government Electronics and Information Association (GEIA), Engineering and Technical Management Symposium, USA, Dallas, 2000: 63-70.

[25] Kostogryzov A., Nistratov G.: Standardization, mathematical modelling, rational management and certification in the field of system and software engineering. Armament.Policy.Conversion, Moscow, 2004.

[26] Zio En.: An Introduction to the Basics of Reliability and Risk Analysis, World Scientific Publishing Co.Pte.Ltd. , 2006.

[27] Korolev V.Yu., Sokolov I.A., Mathematical Models of Non-Homogeneous Flows of Extremal Events. -- Moscow: TORUS-PRESS, 2008.

[28] Kostogryzov A.I., Stepanov P.V.: Innovative management of quality and risks in systems life cycle (modern standards and ideas of system engineering, mathematical models, methods, techniques and software tools complexes for system analysis, including modelling through Internet, 100 examples with an explanation of logic of the reached results, useful practical recommendations). Moscow, Armament.Policy.Conversion, Moscow, 2008.

[29] Kolowrocki K., Soszynska-Budny J.: Reliability and Safety of Complex Technical Systems and Processes, Springer-Verlag London Ltd., 2011.

[30] Kostogryzov A., Nistratov G. and Nistratov A.: Some Applicable Methods to Analyze and Optimize System Processes in Quality Management. Total Quality Management and Six Sigma, InTech, 2012: 127-196.

[31] Grigoriev L., Guseinov Ch., Kershenbaum V., Kostogryzov A. The methodological approach, based on the risks analysis and optimization, to research variants for developing hydrocarbon deposits of Arctic regions. Journal of Polish Safety and Reliability Association. Summer Safety and Reliability Seminars, 2014, 5(1-2): 71-78.

[32] Akimov V., Kostogryzov A., Mahutov N. at al. Security of Russia. Legal, Social&Economic and Scientific&Engineering Aspects. The Scientific Foundations of Technogenic Safety. Under the editorship of Mahutov N.A. Znanie, Moscow, 2015.

[33] Kostogryzov A., Nistratov A., Zubarev I., Stepanov P., Grigoriev L. About accuracy of risks prediction and importance of increasing adequacy of used adequacy of used probabilistic models. Journal of Polish Safety and Reliability Association. Summer Safety and Reliability Seminars, 2015, 6(2): 71-80.

[34] Eid, M. and Rosato, V. Critical Infrastructure Disruption Scenarios Analyses via Simulation. Managing the Complexity of Critical Infrastructures. A Modelling and Simulation Approach, SpringerOpen, 2016: 43-62.

[35] Artemyev V., Kostogryzov A., Rudenko Ju., Kurpatov O., Nistratov G., Nistratov A.: Probabilistic methods of estimating the mean residual time before the next parameters abnormalities for monitored critical systems. In: Proceedings of the 2nd International Conference on System Reliability and Safety (ICSRS), Milan, Italy, 2017: 368-373.

[36] Kostogryzov A., Stepanov P., Nistratov A., Nistratov G., Klimov S., Grigoriev L.: The method of rational dispatching a sequence of heterogeneous repair works. Energetica, 2017, 63(4): 154-162.

[37] Kostogryzov A., Stepanov P., Nistratov A., Atakishchev O.: About Probabilistic Risks Analysis During Longtime Grain Storage. In: Proceedings of the 2nd Internationale Conference on the Social Science and Teaching Research (ACSS-SSTR), Volume 18 of Advances in Social and Behavioral Science. Edited by Harry Zhang. Singapore Management and Sports Science Institute, PTE.Ltd., 2017: 3-8 .

[38] Kostogryzov A., Stepanov P., Grigoriev L., Atakishchev O., Nistratov A., Nistratov G.: Improvement of Existing Risks Control Concept for Complex Systems by the Automatic Combination and Generation of Probabilistic Models and Forming the Storehouse of Risks Predictions Knowledge. In: Proceedings of the 2nd International Conference on Applied Mathematics, Simulation and Modelling (AMSM), Phuket, Thailand. DEStech Publications, Inc., 2017: 279-283.

[39] Kostogryzov A., Atakishchev O., Stepanov P., Nistratov A., Nistratov G., Grigoriev L.: Probabilistic modelling processes of mutual monitoring operators actions for transport systems. In: Proceedings of the 4th International Conference on Transportation Information and Safety (ICTIS), Canada, Banff, 2017: 865-871.

[40] Kostogryzov A., Panov V., Stepanov P., Grigoriev L., Nistratov A., Nistratov G.: Optimization of sequence of performing heterogeneous repair work for transport systems by criteria of timeliness. In: Proceedings of the 4th International Conference on Transportation Information and Safety (ICTIS), Canada, Banff, 2017: 872-876.

[41] Kostogryzov A., Nistratov A., Nistratov G., Atakishchev O., Golovin S., Grigoriev L.: The probabilistic analysis of the possibilities to keep “organism integrity” by continuous monitoring. In: Proceedings of the International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA), Chengdu, China. Atlantis Press, Advances in Intelligent Systems Research, 2018, 159: 432-435.

[42] Kostogryzov A., Grigoriev L., Golovin S., Nistratov A., Nistratov G., Klimov S.: Probabilistic Modeling of Robotic and Automated Systems Operating in Cosmic Space. In: Proceedings of the International Conference on Communication, Network and Artificial Intelligence (CNAI), Beijing, China. DEStech Publications, Inc., 2018: 298-303.

[43] Kostogryzov A., Grigoriev L., Kanygin P., Golovin S., Nistratov A., Nistratov G.: The Experience of Probabilistic Modeling and Optimization of a Centralized Heat Supply System Which is an Object for Modernization. International Conference on Physics, Computing and Mathematical Modeling (PCMM), Shanghai, DEStech Publications, Inc., 2018: 93-97.

[44] Artemyev V., Rudenko Ju., Nistratov G.: Probabilistic modeling in system engineering. Probabilistic methods and technologies of risks prediction and rationale of preventive measures by using “smart systems”. Applications to coal branch for increasing Industrial safety of enterprises. Edited by Andrey Kostogryzov, IntechOpen, 2018: 23-51.

[45] Kershenbaum V., Grigoriev L., Kanygin P., Nistratov A.: Probabilistic modeling in system engineering. Probabilistic modeling processes for oil and gas systems. Edited by Andrey Kostogryzov, IntechOpen, 2018: 55-79.

[46] Kostogryzov A.I., Bezkorovainy M.M., Lvov V.M., Nistratova E.N., Bezkorovainaya I.V. Complex for Evaluation of Information Systems Operation Quality – “know-how” (CEISOQ), registered by Rospatent №2000610272.

[47] Kostogryzov A.I., Nistratov G.A., Nistratova E.N., Nistratov A.A. Mathematical modeling of system life cycle processes – “know-how”, registered by Rospatent №2004610858.

[48] Kostogryzov A.I., Nistratov G.A., Nistratova E.N., Nistratov A.A. Complex for evaluating quality of production processes, registered by Rospatent №2010614145.

[49] Kostogryzov A.I., Nistratov G.A., Nistratov A.A. Remote analytical support of informing about the probabilistic and time measures of operating system and its elements for risk-based approach, registered by Rospatent №2018617949.

[50] Kostogryzov A.I., Nistratov G.A., Nistratov A.A. Remote rationale of requirements to means and conditions for providing “smart” systems operation quality, registered by Rospatent №2018618572.

[51] Kostogryzov A.I., Nistratov G.A., Nistratov A.A. Remote probabilistic prediction of informatized systems operation quality, registered by Rospatent №2018618686.

Downloads

How to Cite

Kostogryzov, A. I. (2019). Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions. Artificial Intelligence Advances, 1(2), 5–23. https://doi.org/10.30564/aia.v1i2.1195

Issue

Vol. 1 , Iss. 2 (October 2019)

Article Type

Article
Crossref
Scopus
Google Scholar
Europe PMC