H.O. Kravtsov, S.M. Hrechko, V.V. Nikitchenko, A.M. Prymushko
Èlektron. model. 2022, 44(3):14-30
https://doi.org/10.15407/emodel.44.03.014
ABSTRACT
Authors propose an algebraic system with some special axioms as a mathematical framework to model arbitrary cognitive agents. We develop cognitive process as the composition of functions that happens if and only if some given requirements are met with some given probability; we introduce definitions of objective and subjective contradiction within a cognitive algebraic system (CAS). We proved that subjective contradiction makes CAS unable to find an optimal solution analytically and thus such a CAS is deliberated to fallback to the combinatorics and its methodology. Rigorous definitions of theoretical and practical experimentation and theoretical and practical learning were given, also was shown the role of (natural) language in these processes. New science problem arose — the problem of defining language’s semantics within described CAS.
KEYWORDS
strong artificial intelligence, cognitive algebraic system, consistency of cognitive system, time quantum, nature of subjective time, synthesis model, learning, research.
REFERENCES
- “The BRAIN Initiative”, available at: https://braininitiative.nih.gov/funding/cleared-initiatives (accessed April 11, 2022).
- Open AI, “Effort to democratize artificial intelligence research?”, available at: https:// www.csmonitor.com/Technology/2015/1214/Open-AI-Effort-to-democratize-artificial-intelligence-research (accessed April 11, 2022).
- “DeepMind says reinforcement learning is ‘enough’ to reach general AI”, available at: https://venturebeat.com/2021/06/09/deepmind-says-reinforcement-learning-is-enough-to-reach-general-ai/ (accessed April 11, 2022).
- Tokic, M. and Palm, G. (2011), Value-Difference Based Exploration: Adaptive Control Between Epsilon-Greedy and Softmax. Advances in Artificial Intelligence. Lecture Notes in Computer Science 7006, Springer, ISBN 978-3-642-24455-1.
https://doi.org/10.1007/978-3-642-24455-1_33 - “Building Trust in Human-Centric Artificial Intelligence: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions”, European Commission, Brussels, 2019, available at: https://ec.europa.eu/jrc/communities/en/community/digitranscope/ document/building-trust-human-centric-artificial-intelligence (accessed: 11 April 2022).
- Artemyeva, Ye.YU. (1999), Osnovy psikhologii subyektivnoy semantiki [Fundamentals of the psychology of subjective semantics], Nauka, Moscow, Russia.
- Subyektivnoye semanticheskoye prostranstvo [Subjective semantic space], Humanitarian portal, available at: https://gtmarket.ru/concepts/7032 (accessed April 11, 2022).
- Georgia, M. Green. (1996), Pragmatics and Natural Language Understanding, Routledge.
- Skrebtsova, T.G. (2011), Kognitivnaya lingvistika: kurs lektsiy [Cognitive linguistics: a course of lectures], Filologicheskiy fakultet SpbGU, St. Petersburg, Russia.
- The Brain Modeling Toolkit (BMTK), Alen institute for brain science, available at: https://alleninstitute.github.io/bmtk/ (accessed April 11, 2022).
- Anokhin, K. (2019), Fundamental brain theory: the main challenge to theoretical physics and mathematics of the brain. «Theoretical physics and mathematics of the brain: bridges across disciplines and applications», Lomonosov Moscow State University, Skoltech, December 5, 2019, available at: https://www.youtube.com/watch?v=WY_WPjlfoBY&t=16s (accessed April 11, 2022).
- Maltsev, A.I. (1970), Algebraicheskiye sistemy [Algebraic systems], Nauka, Moscow, USSR.
- Kon, P. (1968), Universalnaya algebra [Universal Algebra], Mir, Moscow, USSR.
- Keisler, G. and Chen, Ch.Ch. (1977), Teoriya modeley [Model theory], Mir, Moscow, USSR.
- Shreyder, Yu.A. (1971), Ravenstvo, skhodstvo, poryadok [Equality, similarity, order], Nauka, Moscow, USSR.
- Vasilenko, V.S. and Matov, O.YA. (2014), Teoriya informatsiyi ta koduvannya [Information theory and coding], IPRI NAN Ukrayiny, Kyiv, Ukraine.
- McLane, S. (2004), Kategorii dlya rabotayushchego matematika [Categories for working mathematician], Fizmatlit, Moscow, Russia.
- Yakovlev, G.N. (2000), Funktsionalnyye prostranstva [Functional spaces], Moskovskiy fiziko-tekhnicheskiy institut, Moscow, Russia.
- Kravtsov, G.A., Koshel, V.I., Dolgorukov, A.V. and Tsurkan, V.V. (2018), “A trainable model for computing on classifications”, Elektronne modelyuvannya, Vol. 40, no. 3, pp. 63-76.
https://doi.org/10.15407/emodel.40.03.063 - Vyuller, T. (2018), Shcho take chas? [What is time?], Translated by Volkovetskaya, S., Nika-Tsentr, Vydavnytstvo Anetty Antonenko, Kyiv, Ukraine.
- Svyrydenko, V. (2002), Filosofskyy entsyklopedychnyy slovnyk [Reductionism. Philosophical encyclopedic dictionary], Instytut filosofiyi imeni Hryhoriya Skovorody NAN Ukrayiny, Abrys, Kyiv, Ukraine.
- Kravtsov, H.O., Kravtsova, N.V., Khodakivska, O.V., Nikitchenko, V.V. and Prymushko, A.M. (2021), “Math of the brain and language. І”, Elektronne modelyuvannya, 43, no. 3, pp. 87-108.
https://doi.org/10.15407/emodel.43.03.087 - Kravtsov, H.O., Kravtsova, N.V., Khodakivska, O.V., Nikitchenko, V.V. and Prymushko, A.M. (2021), “Math of the brain and language. II”, Elektronne modelyuvannya, Vol. 43, no. 4, pp. 69-89.
https://doi.org/10.15407/emodel.43.04.069