DOI:https://doi.org/10.15407/kvt204.02.049
Cybernetics and Computer Engineering, 2021, 2(204)
SHEPETUKHA Y.M., PhD (Engineering), Senior Researcher,
Leading Researcher of the Intelligent Control Department
ORCID: 0000-0002-6256-5248
e-mail: yshep@meta.ua
VOLKOV O.Ye.,
Head of the Intelligent Control Department
ORCID: 0000-0002-5418-6723
email: alexvolk@ukr.net
KOMAR M.M.,
Senior Researcher of Intelligent Control Department
ORCID: 0000-0002-0119-0964
e-mail: nickkomar08@gmail.com
International Research and Training Center for Information Technologies and Systems of NAS of Ukraine and MES of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine
INTELLECTUALIZATION OF DECISION MAKING PROCESSES IN AUTONOMOUS CONTROL SYSTEMS
Introduction. Scientific-technical level of any country in a modern world is mainly determined by a current state and development rate of informational technologies. At the same time, the main avenue of information technologies’ improvement is their intellectualization. Due to intellectualization, it became possible to create advanced systems with principally novel functional capabilities, in particular, high-speed computer systems able to autonomous actions in a complex and dynamic environment. Control means for complex objects and processes play an important role in the operation of autonomous systems. Therefore, the study of theoretical as well as applied issues of such systems’ construction is an important scientific and engineering problem.
The purpose of the paper is to examine both current state and development prospects of a new direction in the area of intelligent information technologies – the elaboration of autonomous control systems for complex objects and processes in a dynamic environment; to formulate a well-grounded approach for the increase in intellectualization level of decision processes in such systems.
Methods. The development of autonomous control systems, as well as the increase in decision making processes’ intellectualization level in such systems, is based on the usage of the following conceptual, theoretical and methodological instruments: the theory of informational technologies’ intellectualization, the methodology of intelligent control, the theoretical fundamentals of artificial intelligence systems’ construction, decision making methods, the methodology of image-based reasoning, methods for simulation of image-based comprehension of environment.
Results. An approach for the consistent usage of methods of artificial intelligence, decision making and intelligent control aimed at the development of autonomous means for the control of complex objects and processes has been examined. Appropriateness of creation of the systems profiled for operations in designated problem domains has been grounded. Both specific features and components of the framework for decision making in intelligent control systems have been determined. Both necessity of the creation of intelligent environment and important role of sensor networks have been stressed. Methodology for the construction of informational images, which represent the most important components of a current situation, has been proposed. Examples of the usage of informational images for performing both dynamic and evolutional re-planning have been considered.
Conclusions. A reasonable way for the development of intelligent control systems is the one that provides a consistent usage of different types of models. Image-based representation of a current situation’s essential interconnections is an efficient instrument for the intellectualization at different stages of decision making processes – alternative generation, understanding of inconsistencies among different data sources, execution of choice procedure, evaluation of results. The application of artificial intelligence elements for decision making in autonomous systems is especially well-grounded in cases of time shortage as well as availability of a great number of existing alternatives.
Keywords: intellectualization of information technologies, intelligent control, decision making, autonomy, artificial intelligence, image, uncertainty, adaptation.
REFERENCES
1. Mertoguno J.S. Human decision making model for autonomic cyber systems. International Journal on Artificial Intelligence Tools. 2014, Vol. 23, N. 6. URL: https://www.worldscientific.com/doi/abs/10.1142/ S0218213014600239. – Title from the screen.
https://doi.org/10.1142/S0218213014600239
2. Gonzales D., Harting S. Designing unmanned systems with greater autonomy. RAND Corporation Research Report, Santa Monica, CA, USA, 2014. URL: https://www.rand.org/content/dam/rand/pubs/research_reports/ RR600/RR626/RAND_RR626.pdf. – Title from the screen.
3. Bradshaw J.M., Hoffman R.R., Johnson M., Woods D.D. The seven deadly myths of “autonomous systems”. IEEE Intelligent Systems. 2013, Vol. 28, N. 3, pp. 54-61.
https://doi.org/10.1109/MIS.2013.70
4. Groumpos P.P. Complex systems and intelligent control: issues and challenges. IFAC Proceedings Volumes. 2001, Vol. 34, N.8, pp. 29-36. URL: https://www.sciencedirect.com/science/article/pii/S1474667017407907. – Title from the screen.
https://doi.org/10.1016/S1474-6670(17)40790-7
5. Artificial Intelligence (AI): What is it and how does it work? URL: https://www.lexology.com/library/detail.aspx?g=5424a424-c590-45f0-9e2a-ab05daff032d. – Title from the screen.
6. Schubert J., Brynielsson J., Nilsson M., Svenmarck P. Artificial intelligence for decision support in command and control systems. Proceedings of the 23rd International Command and Control Research & Technology Symposium “Multi-Domain C2”, Pensacola, FL, USA, 2018. URL: https://www.researchgate.net/publication/330638139_Artificial_Intelligence_for_Decision_Support_in_Command_and_Control_Systems. – Title from the screen.
7. Cunneen M., Mullins M., Murphy F. Autonomous vehicles and embedded artificial intelligence: the challenges of framing machine driving decisions. Applied Artificial Intelligence. 2019, Vol. 33, N.8, pp. 706-731.
https://doi.org/10.1080/08839514.2019.1600301
8. Phillips-Wren G. AI tools in decision making support systems: a review. International Journal on Artificial Intelligence Tools. 2012, Vol. 21, N.2. URL: https://www.researchgate.net/publication/235705583 _Ai_Tools_in_Decision_Making_Support_Systems_a_Review. – Title from the screen.
https://doi.org/10.1142/S0218213012400052
9. Petitti A., Di Paola D. A network of stationary sensors and mobile robots for distributed ambient intelligence. IEEE Intelligent Systems. 2016,Vol. 31. N.6, pp. 28-34.
https://doi.org/10.1109/MIS.2016.43
Received 04.04.2021