The purpose of this paper is to explore the applications of infobody concepts, infobody structures and infobody charts to Artificial Intelligence (AI), specifically, Logical Artificial Intelligence (LAI). It is also trying to explore a new way to resolve some logical issues in current Artificial Intelligence studies with ChatGPT such as answering reasoning questions in family relations. For this purpose, detailed family relations are discussed based on relation theory. Some new concepts such as primary relations, reversed relations and derived relations for family relations are introduced. Also, a relational database is introduced to implement these family relations and the relationships between these family relations, and make them calculatable with SQL. Each SQL query becomes an infobody processor and together with the input and output infobodies compose a unit infobody structure. Multiple unit structures compose an answer structure to answer a specific question in family relations. A specific unit structure can join multiple answer structures to answer multiple questions. A processor with related input infobodies contains all detailed information for reasoning to a specific output infobody and therefore an answer structure can answer a specific reasoning (logical) question. Each answer structure can be presented in an infobody chart which is a visualization of an infobody model. An infobody model can be implemented in another relational database that can be queried by SQL as well. Suppose all academic areas are implemented in knowledge structures with infobody models in clouds, and all commonsense areas such as family relations are implemented in thinking structures with infobody models in clouds, then, any logical AI app should be able to query some of them to answer any logical questions. Also, it is possible to make those IB models for LAI available for all kinds of robots to simulate creative thinking.
| Published in | American Journal of Artificial Intelligence (Volume 8, Issue 2) |
| DOI | 10.11648/j.ajai.20240802.17 |
| Page(s) | 81-113 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Brain Image, Information (New Definition), Infobody, Internal Infobody, External Infobody, Infobody Structure, Infobody Chart, Infobody Model, Thinking Structure, Unit Structure, Answer Structure
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APA Style
Che, Y. (2024). Infobody Structures for Logical Artificial Intelligence with Database Implementation. American Journal of Artificial Intelligence, 8(2), 81-113. https://doi.org/10.11648/j.ajai.20240802.17
ACS Style
Che, Y. Infobody Structures for Logical Artificial Intelligence with Database Implementation. Am. J. Artif. Intell. 2024, 8(2), 81-113. doi: 10.11648/j.ajai.20240802.17
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Download@article{10.11648/j.ajai.20240802.17,
author = {Yuhu Che},
title = {Infobody Structures for Logical Artificial Intelligence with Database Implementation
},
journal = {American Journal of Artificial Intelligence},
volume = {8},
number = {2},
pages = {81-113},
doi = {10.11648/j.ajai.20240802.17},
url = {https://doi.org/10.11648/j.ajai.20240802.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20240802.17},
abstract = {The purpose of this paper is to explore the applications of infobody concepts, infobody structures and infobody charts to Artificial Intelligence (AI), specifically, Logical Artificial Intelligence (LAI). It is also trying to explore a new way to resolve some logical issues in current Artificial Intelligence studies with ChatGPT such as answering reasoning questions in family relations. For this purpose, detailed family relations are discussed based on relation theory. Some new concepts such as primary relations, reversed relations and derived relations for family relations are introduced. Also, a relational database is introduced to implement these family relations and the relationships between these family relations, and make them calculatable with SQL. Each SQL query becomes an infobody processor and together with the input and output infobodies compose a unit infobody structure. Multiple unit structures compose an answer structure to answer a specific question in family relations. A specific unit structure can join multiple answer structures to answer multiple questions. A processor with related input infobodies contains all detailed information for reasoning to a specific output infobody and therefore an answer structure can answer a specific reasoning (logical) question. Each answer structure can be presented in an infobody chart which is a visualization of an infobody model. An infobody model can be implemented in another relational database that can be queried by SQL as well. Suppose all academic areas are implemented in knowledge structures with infobody models in clouds, and all commonsense areas such as family relations are implemented in thinking structures with infobody models in clouds, then, any logical AI app should be able to query some of them to answer any logical questions. Also, it is possible to make those IB models for LAI available for all kinds of robots to simulate creative thinking.
},
year = {2024}
}
TY - JOUR T1 - Infobody Structures for Logical Artificial Intelligence with Database Implementation AU - Yuhu Che Y1 - 2024/12/23 PY - 2024 N1 - https://doi.org/10.11648/j.ajai.20240802.17 DO - 10.11648/j.ajai.20240802.17 T2 - American Journal of Artificial Intelligence JF - American Journal of Artificial Intelligence JO - American Journal of Artificial Intelligence SP - 81 EP - 113 PB - Science Publishing Group SN - 2639-9733 UR - https://doi.org/10.11648/j.ajai.20240802.17 AB - The purpose of this paper is to explore the applications of infobody concepts, infobody structures and infobody charts to Artificial Intelligence (AI), specifically, Logical Artificial Intelligence (LAI). It is also trying to explore a new way to resolve some logical issues in current Artificial Intelligence studies with ChatGPT such as answering reasoning questions in family relations. For this purpose, detailed family relations are discussed based on relation theory. Some new concepts such as primary relations, reversed relations and derived relations for family relations are introduced. Also, a relational database is introduced to implement these family relations and the relationships between these family relations, and make them calculatable with SQL. Each SQL query becomes an infobody processor and together with the input and output infobodies compose a unit infobody structure. Multiple unit structures compose an answer structure to answer a specific question in family relations. A specific unit structure can join multiple answer structures to answer multiple questions. A processor with related input infobodies contains all detailed information for reasoning to a specific output infobody and therefore an answer structure can answer a specific reasoning (logical) question. Each answer structure can be presented in an infobody chart which is a visualization of an infobody model. An infobody model can be implemented in another relational database that can be queried by SQL as well. Suppose all academic areas are implemented in knowledge structures with infobody models in clouds, and all commonsense areas such as family relations are implemented in thinking structures with infobody models in clouds, then, any logical AI app should be able to query some of them to answer any logical questions. Also, it is possible to make those IB models for LAI available for all kinds of robots to simulate creative thinking. VL - 8 IS - 2 ER -
Retired Independent Researcher, Marietta, Georgia, The United States
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