The Computational Psycholinguistic Situational-Fuzzy Self-Controlled Brain and Mind System under Uncertainty
Authors: Ben Khayut, Lina Fabri, Maya Avikhana
Abstract:
The modern Artificial Narrow Intelligence (ANI) models cannot: a) independently, situationally, and continuously function without of human intelligence, used for retraining and reprogramming the ANI’s models, and b) think, understand, be conscious, and cognize under uncertainty and changing of the environmental objects. To eliminate these shortcomings and build a new generation of Artificial Intelligence systems, the paper proposes a Conception, Model, and Method of Computational Psycholinguistic Cognitive Situational-Fuzzy Self-Controlled Brain and Mind System (CPCSFSCBMSUU). This system uses a neural network as its computational memory, and activates functions of the perception, identification of real objects, fuzzy situational control, and forming images of these objects. These images and objects are used for modeling their psychological, linguistic, cognitive, and neural values of properties and features, the meanings of which are identified, interpreted, generated, and formed taking into account the identified subject area, using the data, information, knowledge, accumulated in the Memory. The functioning of the CPCSFSCBMSUU is carried out by its subsystems of the: fuzzy situational control of all processes, computational perception, identifying of reactions and actions, Psycholinguistic Cognitive Fuzzy Logical Inference, Decision Making, Reasoning, Systems Thinking, Planning, Awareness, Consciousness, Cognition, Intuition, and Wisdom. In doing so are performed analysis and processing of the psycholinguistic, subject, visual, signal, sound and other objects, accumulation and using the data, information and knowledge of the Memory, communication, and interaction with other computing systems, robots and humans in order of solving the joint tasks. To investigate the functional processes of the proposed system, the principles of situational control, fuzzy logic, psycholinguistics, informatics, and modern possibilities of data science were applied. The proposed self-controlled system of brain and mind is oriented on use as a plug-in in multilingual subject applications.
Keywords: Computational psycholinguistic cognitive brain and mind system, situational fuzzy control, uncertainty, AI.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 409References:
[1] E. Shetreet, “Study programs, Brain Sciences,” Linguistic Department, Tel Aviv University.
[2] Neuroscience at the Allen Institute. “5 unsolved mysteries about the brain,” March 14, 2019.
[3] B. Khayut, L. Fabri, and M. Avikhana, “Modeling of Computational Systemic Deep Mind Under Uncertainty,” In: 8th International Conference on Complex Adaptive Systems, pp. 253-258, USA, 2016.
[4] B. Khayut, “Modeling of Fuzzy Logic Inference in decision-making system,” in Modeling system, Institute of Mathematics of the Moldavian Academy of Science, vol. 110, pp. 134-143, 1989.
[5] B. Khayut, L. Fabri, and M. Avikhana, “Modeling, planning, decision-making, and control in fuzzy environment,” in Advance Trends in Soft Computing Conference, vol. 312, Springer, USA, pp. 137-143, 2013.
[6] B. Khayut, L. Fabri, and M. Avikhana, “Intelligent Multi-Agent Fuzzy Control System Under Uncertainty,” Journal of Computer Science and Information Technology 4(18), USA, 369-380, 2014.
[7] B. Khayut, L. Fabri, and M. Avikhana, “Knowledge Representation, Reasoning and System Thinking Under Uncertainty,” In: 16th International Conference on Computer Modeling and Simulation, pp. 119-128, Cambridge, UK, 2014.
[8] B. Khayut, L. Fabri, and M. Avikhana, “A Self-Developing Computational System of Full Awareness and Understanding of Reality,” In: ISAE-MAICS Conference, pp. 37-42, Spokane, USA, 2018.
[9] B. Khayut, L. Fabri, and M. Avikhana, “Toward general AI: Consciousness Computational Modeling Under Uncertainty,” In: 2nd International Conference on mathematics and computers in science and engineering (MACISE), Madrid, Spain, pp. 90-97, 2020.
[10] B, Khayut, L. Fabri, and M, Avikhana, “A Computational Intelligent Cognition System Under Uncertainty,” in: Sixth International Congress on Information and Communication Technology (ICICT 2021), vol. 235, Springer, Singapore, pp. 127-136, 2021.
[11] B. Khayut, L. Fabri, and M. Avikhana. “A computational Psycholinguistic System of Intuition and Wisdom Under Uncertainty,” in: World4S 2022 Conference, London, UK and in: Intelligent Sustainable Systems, Selected Papers of Worlds4 2022, vol. 2, Springer, pp. 149-157, 2023.
[12] J. Field, “Psycholinguistics. The Key Concepts,” New York: Routledge, 2004.
[13] P. Buttery, Lecture 11: “Computational Psycholinguistics,” Dept of Theoretical & Applied Linguistics, University of Cambridge.
[14] C. Serva, “How to identify and use Premise and Conclusion Indicator Words,” Study.com, 2021.
[15] L. Zadeh, “Fuzzy Sets. Information and Control,” vol. 8, pp. 338-359, 1965, 1970.
[16] L. Zadeh, “The Concept of a Linguistic Variable and its Application to Approximate Reasoning,” Information Sciences, vol. 14, pp. 141-164, 1970, 1995.
[17] l. Zadeh, “Knowledge Representation in Fuzzy Logic,” In: IEEE Transactions on Knowledge and Data Engineering, Vol. 1(1), pp. 89-100, 1989.
[18] C. Lee, "Fuzzy logic in control systems: fuzzy logic controller," in IEEE Transactions on Systems, Man, and Cybernetics, vol. 20(2), pp. 419-435, 1990.
[19] R. Yager (ed.), “Fuzzy Set and Possibility Theory,” Recent Developments. New York, Pergamon Press, vol 13. P. 633, 1982.
[20] D. Pospelov, “Fuzzy reasoning in pseudo-physical logics”, In: Fuzzy Sets and Systems, Elsevier, Vol 22 (1-2), pp. 115-120, 1987.
[21] A. Dopico, “What is fuzzification and defuzzification?”, JanetPanic.com, Blog, 2019.
[22] D. Pospelov, “Situational Control, Science,” p. 288, 1986.
[23] T. Moto-Oka, “Fifth Generation Computer Systems,” In: Proceedings of the International Conference on Fifth Generation Computer Systems, Tokyo, Japan, North-Holland Amsterdam – New York-Oxford, pp. 19-22, (1981).
[24] C. Papadimitriou, S. S. Vempala, D. Mitropolsky, M. Collins, and W. Maass. “Brain computation by assembles of neurons,” in Proceedings of the National Academy of Sciences, vol. 117, No. 25, 2020.
[25] S. Dogging, M. Haiyan, and J. Kralik,” A Brain-Like Computational Model Based on a Shared Memory,” in 6th International Conference on Clooud Computing and Big Data Analytics (ICCBDA), Chengdu, China, 2021.
[26] N. Jones, H. Ross, T. Lynam, P. Perez, and A. Leitch, “Mental Models: An Interdisciplinary Synthesis of Theory and Methods”, Ecology and Society 16(1): 46, 2011.
[27] P. Gardenfors, “Sensation, perception, and imagination”, Oxford Academic, pp. 55-82, 2006.
[28] P. Thagard, “How Brains Make Mental Models”, Department of Philosophy, University Waterloo, Canada, in: L. Magnani, W. Carnielli, C. Pizzi (eds) Model-Based Reasoning in Science and Technology. Studies in Computational Intelligence, vol 314, Springer, Berlin, Heidelberg, pp. 447-461, 2010.
[29] D. Shi, H. Mi, and J. Kralik, “A brain-Like Computational model Based on a Shared Memory”, In: Proceedings of IEEE 6-th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), IEEE, Chengdu, China, pp. 596-599, 2021.
[30] R. Shifrin, D. Bassett, H. Ross, N. Kriegeskorte, and J. Tenenbaum, “The brain produces mind by modeling”, In: Proceedings of National Academy of Sciences of USA (PNAS), Vol 117 (47), pp. 29299-20301, 2020.
[31] K. Przemysiaw, “Uncertainty in the Conjunctive Approach to Fuzzy Inference,” in International Journal of Applied Mathematics and Computer Science, vol. 31, issue 3, 2021, pp. 431-444.
[32] J. Zhang.: Cognitive Functions of the Brain: Perception, Attention and Memory. IFM Lab Tutorial Series 6, 2019.
[33] Xio Lan F., Lian Hong C., Ye L., Jia J. Wen Feng C., Zhang Y., Guo Zhen Z., Yong Jin L., Chang Xu W.: A computational cognition model of perception, memory, and judgment. In: Science China Information Sciences, vol. 57, pp. 1-15, China 2014.