Physics of Mind – A Cognitive Approach to Intelligent Control Theory
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.
KeywordsArtificial Intelligence Theory of control Cognitive models Cognitive agents Hierarchy of industrial or technical systems Cyber-physical system
- 1.Mayorga, R., Perlovsky, L.I. (eds.): Sapient Systems. Springer, London (2008)Google Scholar
- 3.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)Google Scholar
- 9.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)Google Scholar
- 10.Zhang, C., Ren, M., Urtasun, R.: Graph hypernetworks for neural architecture search. In: Proceedings of ICLR (2019). https://arxiv.org/pdf/1810.05749.pdf
- 15.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