Structure of associative heterarchical memory
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 a...
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| Format: | Article |
| Language: | Russian |
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MIREA - Russian Technological University
2022-10-01
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| Series: | Российский технологический журнал |
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| Online Access: | https://www.rtj-mirea.ru/jour/article/view/563 |
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| author | R. V. Dushkin V. A. Lelekova V. Y. Stepankov S. Fadeeva |
| author_facet | R. V. Dushkin V. A. Lelekova V. Y. Stepankov S. Fadeeva |
| author_sort | R. V. Dushkin |
| collection | DOAJ |
| description | 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 intelligence agents (AI-agents), processing of natural language should be conducted at two levels: at the bottom, methods of the ascending paradigm are employed, while symbolic methods associated with the descending paradigm are used at the top. To solve these problems, the authors of the present paper propose a new mathematical formalism: associative heterarchical memory (AH-memory), whose structure and functionality are based both on bionic principles and on the achievements of top-down and bottom-up artificial intelligence paradigms.Methods. Natural language recognition algorithms were used in conjunction with various artificial intelligence methods.Results. The problem of character binding as applied to AH-memory was explored by the research group in earlier research. Here, abstract symbol binding was performed using multi-serial integration, eventually converting the primary symbols produced by the program into integrated abstract symbols. The present paper provides a comprehensive description of AH-memory in the form of formulas, along with their explanations and corresponding schemes.Conclusions. The most universal structure of AH-memory is presented. When working with AH-memory, a developer should select from a variety of possible module sets those AH-memory components that support the most successful and efficient functioning of the AI-agent. |
| format | Article |
| id | doaj-art-d1d770a19ff94151901fe3098fe1b073 |
| institution | DOAJ |
| issn | 2782-3210 2500-316X |
| language | Russian |
| publishDate | 2022-10-01 |
| publisher | MIREA - Russian Technological University |
| record_format | Article |
| series | Российский технологический журнал |
| spelling | doaj-art-d1d770a19ff94151901fe3098fe1b0732025-08-20T02:53:53ZrusMIREA - Russian Technological UniversityРоссийский технологический журнал2782-32102500-316X2022-10-0110571510.32362/2500-316X-2022-10-5-7-15336Structure of associative heterarchical memoryR. V. Dushkin0V. A. Lelekova1V. Y. Stepankov2S. Fadeeva3Artificial Intelligence AgencyArtificial Intelligence AgencyArtificial Intelligence AgencyArtificial Intelligence AgencyObjectives. 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 intelligence agents (AI-agents), processing of natural language should be conducted at two levels: at the bottom, methods of the ascending paradigm are employed, while symbolic methods associated with the descending paradigm are used at the top. To solve these problems, the authors of the present paper propose a new mathematical formalism: associative heterarchical memory (AH-memory), whose structure and functionality are based both on bionic principles and on the achievements of top-down and bottom-up artificial intelligence paradigms.Methods. Natural language recognition algorithms were used in conjunction with various artificial intelligence methods.Results. The problem of character binding as applied to AH-memory was explored by the research group in earlier research. Here, abstract symbol binding was performed using multi-serial integration, eventually converting the primary symbols produced by the program into integrated abstract symbols. The present paper provides a comprehensive description of AH-memory in the form of formulas, along with their explanations and corresponding schemes.Conclusions. The most universal structure of AH-memory is presented. When working with AH-memory, a developer should select from a variety of possible module sets those AH-memory components that support the most successful and efficient functioning of the AI-agent.https://www.rtj-mirea.ru/jour/article/view/563artificial intelligencenatural language processingassociative heterarchical memoryai-agentabstract symbolshypernetpredicate symbol control modelactant role classifierhypergraph |
| spellingShingle | R. V. Dushkin V. A. Lelekova V. Y. Stepankov S. Fadeeva Structure of associative heterarchical memory Российский технологический журнал artificial intelligence natural language processing associative heterarchical memory ai-agent abstract symbols hypernet predicate symbol control model actant role classifier hypergraph |
| title | Structure of associative heterarchical memory |
| title_full | Structure of associative heterarchical memory |
| title_fullStr | Structure of associative heterarchical memory |
| title_full_unstemmed | Structure of associative heterarchical memory |
| title_short | Structure of associative heterarchical memory |
| title_sort | structure of associative heterarchical memory |
| topic | artificial intelligence natural language processing associative heterarchical memory ai-agent abstract symbols hypernet predicate symbol control model actant role classifier hypergraph |
| url | https://www.rtj-mirea.ru/jour/article/view/563 |
| work_keys_str_mv | AT rvdushkin structureofassociativeheterarchicalmemory AT valelekova structureofassociativeheterarchicalmemory AT vystepankov structureofassociativeheterarchicalmemory AT sfadeeva structureofassociativeheterarchicalmemory |