Abstract
Control of structurally-complex industrial and technological objects belongs to the class of problems of intelligent control, which demands making decisions in states of uncertainty. Further development of this industry will be associated with technologies of intelligent control based on knowledge. Such technologies use methods, models, and algorithms extracting and accumulating knowledge needed to find optimal decisions. Intelligent control theory is based on learning surrounding world and adapting to changes in the process of reaching the defined goal. In this paper we consider a cognitive approach to learning developed following the human cognitive ability and a scientific method of physics. The cognitive approach opens new wide directions towards control of industrial objects and situations that are not well structured and difficult to formalize, especially in real-life circumstances with significant uncertainty. A class of cognitive model control agents based on the principles of learning is described in the paper. Cognitive agents are such kind of agents that are learning from their surrounding and modifying their actions to achieve the goals; this type of agents enables solving problems in a wide area of control in the presence of uncertainty.
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References
Mayorga, R., Perlovsky, L.I. (eds.): Sapient Systems. Springer, London (2008)
Perlovsky, L.I., Kozma, R.: Neurodynamics of Higher-Level Cognition and Consciousness. Springer, Heidelberg (2007)
Perlovsky, L.I.: A cognitive model of language and conscious processes. In: Pereira Jr., A., Lehmann, D. (eds.) The Unity of Mind, Brain and World, pp. 265–268. Cambridge University Press, New York (2013)
Perlovsky, L.I.: Language and cognition – joint acquisition, dual hierarchy, and emotional prosody. Front. Behav. Neurosci. 7, 123 (2013). https://doi.org/10.3389/fnbeh.2013.00123
Perlovsky, L.I.: Physics of the mind. Front. Syst. Neurosci. (2016). https://doi.org/10.3389/fnsys.2016.00084
Perlovsky, L.I., Deming, R., Ilin, R.: Emotional Cognitive Neural Algorithms with Engineering Applications; Dynamic Logic: From Vague to Crisp. Springer, Berlin (2011)
Russell, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall Series, Upper Saddle River (2003)
Schoeller, F., Perlovsky, L.I., Arseniev, D.: Physics of the mind: experimental confirmations of theoretical predictions. Phys. Life Rev. 25, 45–68 (2018)
Shkodyrev, V.P.: Technical systems control: from mechatronics to cyber-physical systems. In: Smart Electromechanical Systems, Ser. Studies in Systems, Decision and Control, vol. 49 (2016)
Zhang, C., Ren, M., Urtasun, R.: Graph hypernetworks for neural architecture search. In: Proceedings of ICLR (2019). https://arxiv.org/pdf/1810.05749.pdf
Runck, B.C., Manson, S., Shook, E., Gini, M., Jordan, N.: Using word embeddings to generate data-driven human agent decision-making from natural language. GeoInformatica 23(2), 221–242 (2019)
Cross, E.S., Hortensius, R., Wykowska, A.: From social brains to social robots: applying neurocognitive insights to human-robot interaction. Philos. Trans. R. Soc. B Biol. Sci. 374(1771) (2019). https://doi.org/10.1098/rstb.2018.0024
Chemchem, A., Alin, F., Krajecki, M.: Improving the cognitive agent intelligence by deep knowledge classification. Int. J. Comput. Intell. Appl. 18(1) (2019). https://doi.org/10.1142/s1469026819500056
Jones, A.T., Romero, D., Wuest, T.: Modeling agents as joint cognitive systems in smart manufacturing systems. Manuf. Lett. 17, 6–8 (2018). https://doi.org/10.1016/j.mfglet.2018.06.002
Milis, G.M., Eliades, D.G., Panayiotou, C.G., Polycarpou, M.M.: A cognitive agent architecture for feedback control scheme design. In: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6–8 December 2016 (2017). https://doi.org/10.1109/ssci.2016.7850187
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Perlovsky, L.I., Shkodyrev, V.P. (2020). Physics of Mind – A Cognitive Approach to Intelligent Control Theory. In: Arseniev, D., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds) Cyber-Physical Systems and Control. CPS&C 2019. Lecture Notes in Networks and Systems, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-030-34983-7_2
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