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Journal articles on the topic 'Memory (Artificial)'

Author: Grafiati

Published: 27 July 2024

Last updated: 31 July 2025

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1

Huang, Guang-qiu. "Artificial memory optimization." Applied Soft Computing 61 (December 2017): 497–526. http://dx.doi.org/10.1016/j.asoc.2017.08.021.

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Wan, Changjin, Pingqiang Cai, Ming Wang, Yan Qian, Wei Huang, and Xiaodong Chen. "Artificial Sensory Memory." Advanced Materials 32, no. 15 (2019): 1902434. http://dx.doi.org/10.1002/adma.201902434.

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3

Gensburger, Sarah, and Frédéric Clavert. "Is Artificial Intelligence the Future of Collective Memory?" Memory Studies Review 1, no. 2 (2024): 195–208. https://doi.org/10.1163/29498902-202400019.

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Abstract This Memory Studies Review special issue explores the intricate relationship between artificial intelligence (ai) and collective memory. In the one hand, the emergence of generative ai, exemplified by ChatGPT’s 2022 release, appears to herald a new infrastructure for collective memory. On the other, the memory studies work highlights the limits and the backlashes of this new form of memory in its social dimension. This leads to raise a provocative, open-ended question: Is artificial intelligence the future of collective memory? Our issue brings together diverse perspectives from memor

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Kim, Dongshin, and Jang-Sik Lee. "Liquid-based memory and artificial synapse." Nanoscale 11, no. 19 (2019): 9726–32. http://dx.doi.org/10.1039/c9nr02767j.

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Park, Youngjun, Min-Kyu Kim, and Jang-Sik Lee. "Emerging memory devices for artificial synapses." Journal of Materials Chemistry C 8, no. 27 (2020): 9163–83. http://dx.doi.org/10.1039/d0tc01500h.

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This paper reviews recent developments in artificial synapses that exploit various emerging memory devices. The emulation of synaptic plasticity and operation mechanism of artificial synapses using various materials and structures are presented.

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6

Welberg, Leonie. "Artificial activation of a memory trace." Nature Reviews Neuroscience 13, no. 5 (2012): 287. http://dx.doi.org/10.1038/nrn3242.

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7

Li, Xianneng, and Guangfei Yang. "Artificial bee colony algorithm with memory." Applied Soft Computing 41 (April 2016): 362–72. http://dx.doi.org/10.1016/j.asoc.2015.12.046.

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8

Chen, Yujie, Chi Chen, Hafeez Ur Rehman, et al. "Shape-Memory Polymeric Artificial Muscles: Mechanisms, Applications and Challenges." Molecules 25, no. 18 (2020): 4246. http://dx.doi.org/10.3390/molecules25184246.

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Shape-memory materials are smart materials that can remember an original shape and return to their unique state from a deformed secondary shape in the presence of an appropriate stimulus. This property allows these materials to be used as shape-memory artificial muscles, which form a subclass of artificial muscles. The shape-memory artificial muscles are fabricated from shape-memory polymers (SMPs) by twist insertion, shape fixation via Tm or Tg, or by liquid crystal elastomers (LCEs). The prepared SMP artificial muscles can be used in a wide range of applications, from biomimetic and soft rob

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9

Rafiq, Tanveer, Muhammad Azam, Muhammad Khalid, Salman Akber, Muhammad Sohaib Naseem, and Mian Mohsin Sattar. "Towards a Unified Model of Narrative Memory in Conscious Agents: From Human Cognition to Artificial Consciousness." Asian Bulletin of Big Data Management 4, no. 4 (2024): 55–68. https://doi.org/10.62019/abbdm.v4i4.243.

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This study seeks to bridge the gap between narrative memory in human cognition and artificial agents by proposing a unified model. Narrative memory, fundamental to human consciousness, organizes experiences into coherent stories, influencing memory structuring, retention, and retrieval. By integrating insights from human cognitive frameworks and artificial memory architectures, this work aims to emulate these narrative processes in artificial systems. The proposed model adopts a multi-layered approach, combining elements of episodic and semantic memory with narrative structuring techniques. It

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P, Mrs Divya, and Karthika K S. "A Cognitive Framework for Memory Reconstruction in Artificial Systems." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 770–75. https://doi.org/10.22214/ijraset.2025.68286.

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Artificial cognitive systems suitable of storing and recalling complex patterns are vital for advancing independent intelligence. These systems bear the integration of episodic and semantic memory structures to reconstruct shattered information without significant interference. This study presents a new frame for artificial memory reconstruction inspired by mortal cognitive processes. The frame includes the storage and recovery of spatio-temporal patterns and the operation of Intelligent Software Agents to emulate mortal- suchlike memory functionality. By using contextual integration and simil

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Shtat, Hazar. "Memory Between Bio-cell and Semiconductors: An Introduction to Hybrid Artificial Intelligence." Journal of Electrical Electronics Engineering 3, no. 4 (2024): 01–04. http://dx.doi.org/10.33140/jeee.03.04.01.

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During my time as a theoretical researcher at the quantum laboratories of the University of Paris-Sud in Orsay / Paris - France, in the late 1980s, I had the opportunity to meet one of the most prominent writers on simplifying quantum cybernetics and machine language, Mr. Viktor Davidovich Pekelis, a writer, specializing in quantum cybernetics who was visiting at the time for the Orsay Research Center (University of Paris-Sud).

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Caravelli, Francesco, Gia-Wei Chern, and Cristiano Nisoli. "Artificial spin ice phase-change memory resistors." New Journal of Physics 24, no. 2 (2022): 023020. http://dx.doi.org/10.1088/1367-2630/ac4c0a.

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Abstract We present a proposal for realization of an electrical memory reminiscent of a memristor in connected Kagome artificial spin ice. We show that current flowing through the system alters the magnetic ensemble, which in turns controls the overall resistance thus leaving memory of current passage in the system. This introduces a current-dependent effect for a dynamic resistive state. We simulate a spin-induced thermal phase-change mechanism, and an athermal domain-wall spin inversion. In both cases we observe electrical memory behavior with an I–V hysteretic pinched loop, typical of memri

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13

Izawa, Hideki, Yukio Sekiguchi, and Yasuhito Shiota. "The artificial muscle from shape memory alloy." Journal of Life Support Engineering 17, Supplement (2005): 124. http://dx.doi.org/10.5136/lifesupport.17.supplement_124.

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14

Querlioz, Damien. "(Invited) Memory-Centric Artificial Intelligence with Nanodevices." ECS Meeting Abstracts MA2020-01, no. 24 (2020): 1387. http://dx.doi.org/10.1149/ma2020-01241387mtgabs.

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15

Manca, Vincenzo. "Artificial Neural Network Learning, Attention, and Memory." Information 15, no. 7 (2024): 387. http://dx.doi.org/10.3390/info15070387.

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The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a dual network is outlined acting between synapses of a basic ANN, which controls the learning process and coordinates the subnetworks selected by attention mechanisms toward purposeful behaviors. Mechanisms of memory and their affinity with comprehension are considered, by emphasizing the common role of abstraction and the interplay between assimilation and accommodation, in the spirit of Piaget’s analysis of psychological acqu

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16

Takashima, Kazuto, Jonathan Rossiter, and Toshiharu Mukai. "McKibben artificial muscle using shape-memory polymer." Sensors and Actuators A: Physical 164, no. 1-2 (2010): 116–24. http://dx.doi.org/10.1016/j.sna.2010.09.010.

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17

Lo, Shih-Hsiang, Che-Rung Lee, Quey-Liang Kao, I.-Hsin Chung, and Yeh-Ching Chung. "Improving GPU Memory Performancewith Artificial Barrier Synchronization." IEEE Transactions on Parallel and Distributed Systems 25, no. 9 (2014): 2342–52. http://dx.doi.org/10.1109/tpds.2013.133.

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18

Buddefeld, J., and K. E. Grosspietsch. "Intelligent-memory architecture for artificial neural networks." IEEE Micro 22, no. 3 (2002): 32–40. http://dx.doi.org/10.1109/mm.2002.1013302.

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19

., Santosh Saraf. "ASSOCIATIVE MEMORY IMPLEMENTATION WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Research in Engineering and Technology 03, no. 15 (2014): 152–54. http://dx.doi.org/10.15623/ijret.2014.0315028.

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20

ISHIKAWA, Toshiya, and Takeshi NAKADA. "Shape Memory Alloy Actuator for Artificial Muscle." Journal of Environment and Engineering 5, no. 1 (2010): 105–13. http://dx.doi.org/10.1299/jee.5.105.

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21

M, Robert. "A Successful Artificial Memory Has Been Created." Scientific American 30, no. 6 (2019): 12. http://dx.doi.org/10.1038/scientificamericanmind1119-12.

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22

Dushkin, R. V., V. A. Lelekova, V. Y. Stepankov, and S. Fadeeva. "Structure of associative heterarchical memory." Russian Technological Journal 10, no. 5 (2022): 7–15. http://dx.doi.org/10.32362/2500-316x-2022-10-5-7-15.

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Objectives. Since the 20th century, artificial intelligence methods can be divided into two paradigms: top-down and bottom-up. While the methods of the ascending paradigm are difficult to interpret as natural language outputs, those applied according to the descending paradigm make it difficult to actualize information. Thus, natural language processing (NLP) by artificial intelligence remains a pressing problem of our time. The main task of NLP is to create applications that can process and understand natural languages. According to the presented approach to the construction of artificial int

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23

Green, HS, and T. Triffet. "Quantum Mechanics, Real and Artificial Intelligence." Australian Journal of Physics 44, no. 3 (1991): 323. http://dx.doi.org/10.1071/ph910323.

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Some incompletely resolved problems in the quantal theories of measurement and observation are discussed with reference to Schrodinger's 'cat paradox' and the paradox of Wigner's friend. A simple version of the theory of measurement is presented, which does not completely resolve these paradoxes but suggests the need for an objective quantal description of the process of observation, and the formation of memory, of an event originating at the microscopic level, by an animal or artificial intelligence. A quantised model is then developed to simulate the function of the cerebral cortex in the fo

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24

Kulyukin, Vladimir A. "On Correspondences between Feedforward Artificial Neural Networks on Finite Memory Automata and Classes of Primitive Recursive Functions." Mathematics 11, no. 12 (2023): 2620. http://dx.doi.org/10.3390/math11122620.

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When realized on computational devices with finite quantities of memory, feedforward artificial neural networks and the functions they compute cease being abstract mathematical objects and turn into executable programs generating concrete computations. To differentiate between feedforward artificial neural networks and their functions as abstract mathematical objects and the realizations of these networks and functions on finite memory devices, we introduce the categories of general and actual computabilities and show that there exist correspondences, i.e., bijections, between functions comput

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25

Li, Xuesong, Pan Zeng, Feilong Wang, et al. "A nanoimprinted artificial engram device." Nanoscale Horizons 6, no. 9 (2021): 718–28. http://dx.doi.org/10.1039/d1nh00064k.

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A nanoimprinted artificial engram device is presented, which meets all the requirements for engrams including synaptic plasticity, long memory storage time, asymmetric memorizing-forgetting behavior and measurable changes and responses.

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26

Jitendra, Sunte. "The Natural Way of Permanent Memory Gaining Methods and Processes." Journal of Trauma, Orthopaedic and Urological Nursing 1, no. 2 (2023): 11–14. https://doi.org/10.5281/zenodo.8434346.

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<em>The human can gain knowledge in the form of text, voice, doer of the action etc., in these articles, patents, journals, conferences, workshops, FDP, seminars, internet, social Medias etc. All these one can share , transfer, deliver knowledge to others, So all information’s are to be stored in the form of CD , floppy disc, pen drive , laptops, PC , etc. high volume space memory stored in the form of block chain, cloud as memory storage one . so all one are artificial way of storing memory information., this paper deals with the information are stored longevity long term storage units

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27

Widrow, Bernard, and Juan Carlos Aragon. "Cognitive memory." Neural Networks 41 (May 2013): 3–14. http://dx.doi.org/10.1016/j.neunet.2013.01.016.

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28

Park, Taewon, Inchul Choi, and Minho Lee. "Distributed associative memory network with memory refreshing loss." Neural Networks 144 (December 2021): 33–48. http://dx.doi.org/10.1016/j.neunet.2021.07.030.

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29

Qin, Ling, Siqi Cheng, Bingyang Xie, Xianhua Wei, and Wenjing Jie. "Co-existence of bipolar nonvolatile and volatile resistive switching based on WO₃ nanowire for applications in neuromorphic computing and selective memory." Applied Physics Letters 121, no. 9 (2022): 093502. http://dx.doi.org/10.1063/5.0113433.

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A two-terminal memristor can be used for information memory and logic operation as well as serving as an artificial synapse for neuromorphic computing. Selective memory with some enjoyable information to be remembered and other to be screened out can be emulated by an artificial synapse. In this work, a memristor based on a single WO3 nanowire can be constructed, which demonstrates the co-existence of bipolar nonvolatile and volatile resistive switching (RS) behaviors that can be tuned by the amplitude of the operation voltage. For small operation voltages (2 V), the device demonstrates nonvol

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30

Munir, Aumm-e.-hani, and Wajahat Mahmood Qazi. "Artificial Subjectivity: Personal Semantic Memory Model for Cognitive Agents." Applied Sciences 12, no. 4 (2022): 1903. http://dx.doi.org/10.3390/app12041903.

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Personal semantic memory is a way of inducing subjectivity in intelligent agents. Personal semantic memory has knowledge related to personal beliefs, self-knowledge, preferences, and perspectives in humans. Modeling this cognitive feature in the intelligent agent can help them in perception, learning, reasoning, and judgments. This paper presents a methodology for the development of personal semantic memory in response to external information. The main contribution of the work is to propose and implement the computational version of personal semantic memory. The proposed model has modules for

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31

Bhatnagar, Priyanka, Thanh Tai Nguyen, Sangho Kim, Ji Heun Seo, Malkeshkumar Patel, and Joondong Kim. "Transparent photovoltaic memory for neuromorphic device." Nanoscale 13, no. 10 (2021): 5243–50. http://dx.doi.org/10.1039/d0nr08966d.

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32

Kim, Gyeongpyo, Seoyoung Park, and Sungjun Kim. "Quantum Dots for Resistive Switching Memory and Artificial Synapse." Nanomaterials 14, no. 19 (2024): 1575. http://dx.doi.org/10.3390/nano14191575.

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Memristor devices for resistive-switching memory and artificial synapses have emerged as promising solutions for overcoming the technological challenges associated with the von Neumann bottleneck. Recently, due to their unique optoelectronic properties, solution processability, fast switching speeds, and low operating voltages, quantum dots (QDs) have drawn substantial research attention as candidate materials for memristors and artificial synapses. This review covers recent advancements in QD-based resistive random-access memory (RRAM) for resistive memory devices and artificial synapses. Fol

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33

BROM, CYRIL, JIŘÍ LUKAVSKÝ, and RUDOLF KADLEC. "EPISODIC MEMORY FOR HUMAN-LIKE AGENTS AND HUMAN-LIKE AGENTS FOR EPISODIC MEMORY." International Journal of Machine Consciousness 02, no. 02 (2010): 227–44. http://dx.doi.org/10.1142/s1793843010000461.

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34

Meshref, Hossam. "Novel Implementation of the Artificial Immune System Memory." International Journal of Computer Applications 155, no. 13 (2016): 36–41. http://dx.doi.org/10.5120/ijca2016912585.

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35

TAKAGI, Toshiyuki, Yun LUO, Shinya HARA, et al. "An artificial sphincter using shape memory alloy actuators." Journal of Advanced Science 12, no. 3 (2000): 337–42. http://dx.doi.org/10.2978/jsas.12.337.

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36

TAKAGI, Toshiyuki, Yun LUO, Hirokazu NAKAMURA, et al. "Application of Shape Memory Alloys in Artificial Sphincters." Proceedings of the JSME annual meeting 2000.1 (2000): 55–56. http://dx.doi.org/10.1299/jsmemecjo.2000.1.0_55.

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37

Kuznetsov, Andrew, Ping-Ya Chao, Christopher Dishop, Allen Brown, and Anita Williams Woolley. "Transactive Memory in Caregiver Networks Using Artificial Intelligence." Proceedings of the AAAI Symposium Series 4, no. 1 (2024): 168–72. http://dx.doi.org/10.1609/aaaiss.v4i1.31789.

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As the population ages and an increasing number of adults want to age in place in their homes, they will rely on a network of family, friends, and other caregivers to provide various forms of assistance. Coordination across this loosely connected network is a common challenge, requiring information sharing, schedule alignment and task coordination. Here, we propose that artificial intelligence (AI) may be used to develop tools to help loosely connected care networks develop better collective cognition. Specifically, we focus on helping members of care networks develop a transactive memory syst

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38

Patnaik, L. M., and J. Mohan Kumar. "Distributed memory systems for simulating artificial neural networks." Computers & Electrical Engineering 19, no. 6 (1993): 431–43. http://dx.doi.org/10.1016/0045-7906(93)90019-n.

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39

Kunze, Donald. "Chiasmus, Artificial Memory, and the Arts of Arrangement." Nexus Network Journal 12, no. 3 (2010): 377–88. http://dx.doi.org/10.1007/s00004-010-0041-5.

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40

Molas, Gabriel, and Etienne Nowak. "Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence." Applied Sciences 11, no. 23 (2021): 11254. http://dx.doi.org/10.3390/app112311254.

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This paper presents an overview of emerging memory technologies. It begins with the presentation of stand-alone and embedded memory technology evolution, since the appearance of Flash memory in the 1980s. Then, the progress of emerging memory technologies (based on filamentary, phase change, magnetic, and ferroelectric mechanisms) is presented with a review of the major demonstrations in the literature. The potential of these technologies for storage applications addressing various markets and products is discussed. Finally, we discuss how the rise of artificial intelligence and bio-inspired c

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41

Hochreiter, Sepp, and Jürgen Schmidhuber. "Long Short-Term Memory." Neural Computation 9, no. 8 (1997): 1735–80. http://dx.doi.org/10.1162/neco.1997.9.8.1735.

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Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to

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42

Bahr, Gisela Susanne, William H. Allen, Philip J. Bernhard, and Stephen Wood. "The Artificial Memory of Mr. Polly: Memory Simulation in Databases and the Emergence of Knowledge." Leonardo 52, no. 3 (2019): 300–304. http://dx.doi.org/10.1162/leon_a_01441.

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Human memory may be characterized by five dimensions: (1) large capacity; (2) associativity; (3) diversity of memory systems; (4) change over time; and (5) a unified memory experience. The organization and multidimensionality underlying memory can be represented with set theory. This offers a new mathematical perspective, which is the foundation for the cognitive memory architecture Ardemia. The authors present a relational database implementation of Ardemia that supports the creation of the artificial memory of Mr. Polly, the main character in H.G. Wells’s novel The History of Mr. Polly. In a

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Volynskaya, Alina. "Collective Memory Through Computer Memories." Memory Studies Review 1, no. 2 (2024): 343–63. https://doi.org/10.1163/29498902-202400017.

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Abstract Articulating the interplay between collective memory and artificial intelligence, this paper examines the saildart archive, a unique digital collection derived from the backup tapes of the Stanford Artificial Intelligence Laboratory (sail). Preserved somewhat serendipitously and not originally intended to serve as an archive, saildart has retained nearly the entire contents of the laboratory’s computers from 1972 to 1990. By exploring the archive’s origins, structure, selection and appraisal practices, the article sets the stage for a discussion on the nature of machine-recorded and m

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44

Zigunovs, Maksims. "THE ALZHEIMER’S DISEASE IMPACT ON ARTIFICIAL NEURAL NETWORKS." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 17, 2021): 205–9. http://dx.doi.org/10.17770/etr2021vol2.6632.

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The Alzheimer’s Disease main impact on the brain is the memory loss effect. Therefore, in the “neuron world” this makes a disorder of signal impulses and disconnects neurons that causes the neuron death and memory loss. The research main aim is to determine the average loss of signal and develop memory loss prediction models for artificial neuron network. The Izhikevich neural networking model is often used for constructing neuron neural electrical signal modeling. The neuron model signal rhythm and spikes are used as model neuron characteristics for understanding if the system is stable at ce

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Cristina, Dumitru, Zamfirache Florin, Trimbitas Adina, and Mihaela Radu Beatrice. "BRAIN. Broad Research in Artificial Intelligence and Neuroscience - Total Sleep Deprivation Modulates Working Memory and Depression in Aged Rats." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 4 (2024): 55–72. https://doi.org/10.70594/brain/15.4/5.

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Sleep deprivation has shown promise in rapidly alleviating depression symptoms without adverse effects, its impact on memory remains unclear, especially in depressed individuals. The study investigates the effects of total sleep deprivation on depression symptomatology and memory in aged rats with induced depression. The experiments were carried out on 8 adult male Sprague Dawley rats. A depression model was induced using the chronic retention stress paradigm. Total Sleep Deprivation was achieved using the “gentle handling” procedure. Behavioral tests, including the open field test

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46

Maua, D. D., C. P. De Campos, and M. Zaffalon. "Solving Limited Memory Influence Diagrams." Journal of Artificial Intelligence Research 44 (May 21, 2012): 97–140. http://dx.doi.org/10.1613/jair.3625.

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We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they a

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47

Ramos Grané, Marta. "Manipulating artificial memory: An example of mistake in recalling." Fortunatae. Revista Canaria de Filología, Cultura y Humanidades Clásicas 38, no. 2 (2023): 59–69. http://dx.doi.org/10.25145/j.fortunat.2023.38.04.

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In late medieval Scholastic thought, the sensory experience from the Aristotelian tradition is essential for the knowledge of reality. In the case of the arts of memory, such as Romberch’s Congestorium (Venice, 1520), theories of perception are applied to the mental formation of places and images, key elements of memory systems «per locos et imagines». Through an example taken from the Congestorium, in which the author acknowledges his error in the process of imagining places, one can appreciate how theories of perception are applied to mental processes. Thus, after a detailed description of t

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48

Volodina, O. V., A. A. Skvortsov, and V. K. Nikolaev. "Memory computing based on thermal memory elements." E3S Web of Conferences 458 (2023): 01009. http://dx.doi.org/10.1051/e3sconf/202345801009.

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The article analyses the possibility of using elements of thermal memory to create a system that allows to perform calculations in memory. Such a computing system is built on devices that are used simultaneously for storing input data, performing a logical operation, and storing the output result. The authors conclude that it is possible to emulate this behaviour by using thermal memory elements with dielectric (SiO2) by a layer of thermal insulation. Special attention is paid to the logic gates of computing systems and their realisation on the basis of thermal memory elements. Simulation mode

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Li, Jun, Shengkai Wen, Dongliang Jiang, Linkang Li, and Jianhua Zhang. "Fully solution-processed InSnO/HfGdO _X thin-film transistor for light-stimulated artificial synapse." Flexible and Printed Electronics 7, no. 1 (2022): 014006. http://dx.doi.org/10.1088/2058-8585/ac4bb2.

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Abstract In recent years, the research interest in brain-inspired light-stimulated artificial synaptic electronic devices has greatly increased, due to their great potential in constructing low-power, high-efficiency, and high-speed neuromorphic computing systems. However, in the field of electronic synaptic device simulation, the development of three-terminal synaptic transistors with low manufacturing cost and excellent memory function still faces huge challenges. Here, a fully solution-processed InSnO/HfGdO X thin film transistor (TFT) is fabricated by a simple and convenient solution proce

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Dushkin, R. V., V. A. Lelecova, and K. Yu Eidemiller. "System of Operations on Associative Heterarchical Memory." Programmnaya Ingeneria 14, no. 2 (2023): 69–76. http://dx.doi.org/10.17587/prin.14.69-76.

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Text processing in natural language remains an important task for the field of development of artificial intelligence methods and tools. Since the twentieth century, artificial intelligence methods have been divided into two paradigms — top-down and bottom-up. The methods of the ascending paradigm are difficult to interpret in the form of the output of natural language, and the methods of the descending paradigm are difficult to actualize information. Taking into account the authors approach to the construction of artificial intelligence agents, the processing of natural language must be perfo

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