Automatic reconstruction of the character

Z. V. Nagoev ; I. A. Pshenokova ; O. V. Nagoeva

News of the Kabardin-Balkar scientific center of RAS, no. 6 (2021), pp. 66-77.

Résumé

The purpose of this study is to develop a system for teaching simulation models of users to the properties of character and temperament based on data on their behavior in the Internet. To model user behavior, the metaphor of designing an intelligent software agent is used; the agent is controlled on the basis of a multi-agent neurocognitive architecture, which is well suited for teaching specialized patterns of behavior of specific users, information about which can be collected in the Internet. As a result of the study, the basic principles of imitation modeling of functional systems of unconscious and conscious cognitive processes were developed based on the invariant of a recursive multi-agent neurocognitive architecture. Algorithms for the simulation of functional systems of character and temperament based on the invariant of a recursive multi-agent neurocognitive architecture have been substantiated. The results obtained can be used to develop basic principles, models, methods and algorithms for learning the invariant of a multi-agent neurocognitive architecture based on data on user behavior and mentions about it in the Internet space.

Détail

Keywords: artificial intelligence, behavior modeling, cognitive architectures, multi-agent systems, enveloping intelligence systems, predictive analytics.

Bibliographie

[1] Z. V. Nagoev, K. Ch. Bzhikhatlov, O. V. Nagoeva, Z. A. Sundukov, S. A. Kankulov, “Autonomous formation of a user model based on digital footprint data in the Internet space based on training multi-agent neurocognitive architectures”, News of the Kabardino-Balkarian Scientific Center of RAS, 2020, no. 6 (98), 52–67 (In Russian) | DOI

[2] V. V. Petukhov, Lectures on general psychology, MGU, Moscow, 1997, 597 pp. (In Russian)

[3] V. V. Shulgovsky, “Higher nervous activity”, Great Russian encyclopedia, 2005–2019 (In Russian) https://bigenc.ru/biology/text/2337707

[4] R. Van Gulick, “Consciousness”, The Stanford Encyclopedia of Philosophy, eds. E.N. Zalta, 2018 https://plato.stanford.edu/entries/consciousness/

[5] P. K. Anokhin, Key questions of the theory of functional systems, Nauka, Moscow, 1980, 203 pp. (In Russian)

[6] B. J. Baars, In the Theater of Consciousness. The Workspace of the Mind, Oxford University Press, Oxford, 1997, 88 pp.

[7] M. P. Shanahan, “A cognitive architecture that combines internal simulation with a global workspace”, Consciousness and Cognition, 15 (2006), 433–449 | DOI

[8] S. Franklin, T. Madl, S. D'Mello, J. Snaider, “LIDA: a systems-level architecture for cognition, emotion, and learning”, IEEE Transactions on Autonomous Mental Development, 6 (2014), 19–41 | DOI

[9] F. Crick, C. Koch, “The problem of consciousness”, Scientific American. Special edition, 12:1 (2002), 11–17

[10] S. Grossberg, “Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support”, Neural Networks, 87 (2017), 38–95 | DOI

[11] A. R. Luria, Language and Consciousness, eds. E.D. Chomskaya, MGU, Moscow, 1979, 320 pp. (In Russian)

[12] D. Chalmers, “Facing up to the problem of consciousness”, Journal of Consciousness Studies, 1995, no. 2 (3), 200–219

[13] S. Dehaene, H. Lau, S. Kouider, What is consciousness, and could machines have it?, Science, 358 (2017), 486–492 | DOI

[14] G. Tononi, “Consciousness as integrated information: a provisional manifesto”, Biol. Bull, 215 (2008), 216–242 | DOI

[15] L. Floridi, “Consciousness, agents, the knowledge game”, Mind Mach, 2005, no. 15, 415–444 | DOI

[16] A. Morin, “Possible links between self-awareness and inner speech”, J. Conscious. Stud, 2005, no. 12, 115–134

[17] O. Holland, “Robots with internal models a route to machine consciousness”, J. Conscious. Stud, 2003, no. 10, 77–109

[18] Hesslow G., “Conscious thought as simulation of behaviour and perception”, Trends Cogn. Sci., 2002, no. 6, 242–247 | DOI

[19] J. K. O'Regan, A. Noe, “A sensorimotor account of vision visual consciousness”, Behav. Brain Sci, 2001, no. 24, 939–973 | DOI

[20] M. S.A. Graziano, “The Attention Schema Theory: A Foundation for Engineering Artificial Consciousness image”, Front. Robot. AI, 4:60 (2017) | DOI

[21] K. Igelstrom, T. W. Webb, M. S.A. Graziano, “Functional connectivity between the temporoparietal cortex and cerebellum in autism spectrum disorder”, Cereb. Cortex, 27:4 (2016), 2617–2627 | DOI

[22] J. A. Reggia, G. E. Katz, G. P. Davis, “Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies”, Consciousness in Humanoid Robots, 5:1 (2018) | DOI

[23] R. Manzotti, A. Chella, “Good old-fashioned artificial consciousness and the intermediate level fallacy”, Frontiers Robotics AI, 5:39 (2018) | DOI

[24] A. Linson, A. Clark, S. Ramamoorthy, K. Friston, “The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition”, Consciousness in Humanoid Robots, 5:21 (2018) | DOI

[25] M. Biehl, C. Guckelsberger, C. Salge, S. C. Smith, D. Polani, “Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop”, Front. Neurorobot, 12:45 (2018) | DOI

[26] A. Winfield, “Why Did You Just Do That? Explainability and Artificial Theory of Mind for Social Robots”, Culturally Sustainable Social Robotics, Frontiers in Artificial Intelligence and Applications Ebook, 335, 2020, 8–10 | DOI

[27] L. Cominelli, D. Mazzei, D. E. De Rossi, “SEAI: Social Emotional Artificial Intelligence Based on Damasio's Theory of Mind”, Front. Robot. AI, 5:6 (2018) | DOI

[28] A. Damasio, The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Spektrum Der Wissenschaft, 2000, 104 pp.

[29] T. Bosse, C. M. Jonker, J. Treur, “Formalisation of Damasio's theory of emotion, feeling and core consciousness”, Consciousness and Cognition, 17:1 (2008), 94–113 | DOI | MR

[30] Y. Kinouchi, K. J. Mackin, “A Basic Architecture of an Autonomous Adaptive System With Conscious-Like Function for a Humanoid Robot”, Front. Robot. AI, 5:30 (2018) | DOI

[31] J. Anderson, “Cognitive and psychological computation with neural models”, IEEE Trans. Syst. Man Cybernet, 5 (1983) | DOI

[32] M. Golden, “Stability and optimization analyses of the generalized brain state in a box neural network model”, J. Math. Psychol, 37 (1993), 282–298 | DOI | Zbl

[33] F. Velde, “In Situ Representations and Access Consciousness in Neural Blackboard or Workspace Architectures”, Front. Robot. AI, 5:32 (2018) | DOI

[34] C. Balkenius, A. Tjostheim Trond, B. Johansson, P. Gardenfors, “From Focused Thought to Reveries: A Memory System for a Conscious Robot”, Front. Robot. AI, 5:29 (2018) | DOI

[35] Chatila R. and et al, “Toward Self-Aware Robots”, Front. Robot. AI, 5:88 (2018) | DOI

[36] Pei Wang, Patrick Hammer, Hongzheng Wang, “An Architecture for Real-Time Reasoning and Learning”, AGI 2020: Artificial General Intelligence, 2020, 347–356 | DOI

[37] Z. V. Nagoev, Intelligence, or thinking in living and artificial systems, KBSC of RAS Publishing House, Nal'chik, 2013, 232 pp. (In Russian)

[38] Z. Nagoev, I. Pshenokova, O. Nagoeva, Z. Sundukov, “Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures”, Cognitive Systems Research, 66 (2021), 82–88 | DOI

[39] Z. Nagoev, O. Nagoeva, I. Gurtueva, “Multi-Agent neurocognitive models of semantics of spatial localization of events”, Cognitive Systems Research, 59, 91–102 | DOI

[40] Z. V. Nagoev, K. Ch. Bzhikhatlov, I. A. Pshenokova at el., “Autonomous synthesis of spatial ontologies in the decision-making system of a mobile robot based on self-organization of multiagent neurocognitive architecture”, News of the Kabardino-Balkarian Scientific Center of RAS, 2020, no. 6 (98), 68–79 (In Russian) | DOI

[41] I. A. Pshenokova, Z. A. Sundukov, “Development of a simulation model for predicting the behavior of an intelligent agent based on an invariant of a recursive multi-agent neurocognitive architecture”, News of the Kabardino-Balkarian Scientific Center of RAS, 2020, no. 6 (98), 80–90 (In Russian) | DOI

[42] M. I. Anchokov, “Application of reinforcement learning to solve the problem of structuring the external environment”, News of the Kabardino-Balkarian Scientific Center of RAS, 2020, no. 6 (98), 14–19 (In Russian) | DOI