[1]CUI Tiejun,LI Shasha.Concept formation process of human and artificial intelligence systems[J].CAAI Transactions on Intelligent Systems,2022,17(5):1012-1020.[doi:10.11992/tis.202104002]
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Concept formation process of human and artificial intelligence systems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 5 Page number: 1012-1020 Column: 学术论文—人工智能基础 Public date: 2022-09-05
Title:
Concept formation process of human and artificial intelligence systems
Author(s):
CUI Tiejun1;  LI Shasha2
1. College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China;
2. School of Business Administration, Liaoning Technical University, Huludao 125105, China
Keywords:
intelligent science; artificial intelligence system; human; concept; formation process; factor; function; comparative analysis
CLC:
TP18;X913
DOI:
10.11992/tis.202104002
Abstract:
The formation of the concept is the basis of realizing artificial intelligence. This paper studies the process of people forming the concept of things to study the formation process of concepts in artificial intelligence systems. When comparing the concept formation process of human and artificial intelligence systems, the characteristics are obtained as follows: Humans’ advantage is that they can independently determine various characteristics and divisions in object representation and object function and can establish concepts through thinking and association when the corresponding relationship between object, descriptive definition, and functional definition is incomplete. The advantage of an artificial intelligence system lies in the rich perception ability of an object, the long-term storage, operation, and analysis ability of various characteristics and divisions of objects; and the shortcomings of the concept formation process of artificial intelligence correspond to the advantages of the human concept formation process. Therefore, the author believes that the concept formation process of artificial intelligence must focus on the intelligent identification of factors, the systematic practice of functions, and the teacher learning of human experience and knowledge. In the absence of human experience and knowledge of the existing technology, the artificial intelligence system cannot independently establish the concept and knowledge base and cannot realize the intelligent process.
References:
[1] 江怡. 对人工智能与自我意识区别的概念分析[J]. 自然辩证法通讯, 2019, 41(10): 1–7
JIANG Yi. A conceptual analysis of the distinction between artificial intelligence and self-consciousness[J]. Journal of dialectics of nature, 2019, 41(10): 1–7
[2] 颜佳华, 王张华. 数字治理、数据治理、智能治理与智慧治理概念及其关系辨析[J]. 湘潭大学学报(哲学社会科学版), 2019, 43(5): 25–30,88
YAN Jiahua, WANG Zhanghua. Analyzing the concepts and their relationship of digital governance, data governance, smart governance and intelligent governance[J]. Journal of Xiangtan University (philosophy and social sciences edition), 2019, 43(5): 25–30,88
[3] 李国山. 人工智能与人类智能: 两套概念, 两种语言游戏[J]. 上海师范大学学报(哲学社会科学版), 2018, 47(4): 26–33
LI Guoshan. Artificial intelligence and human intelligence: two concepts and games of two languages[J]. Journal of Shanghai Normal University (philosophy & social sciences edition), 2018, 47(4): 26–33
[4] 钟义信. 人工智能: 概念·方法·机遇[J]. 科学通报, 2017, 62(22): 2473–2479
ZHONG Yixin. Artificial intelligence: concept, approach and opportunity[J]. Chinese science bulletin, 2017, 62(22): 2473–2479
[5] 许立波, 潘旭伟, 袁平, 等. 知识智能涌现创新: 概念、体系与路径[J]. 智能系统学报, 2017, 12(1): 47–54
XU Libo, PAN Xuwei, YUAN Ping, et al. Knowledge innovation by intelligent emergence—concept, framework and its pathway[J]. CAAI transactions on intelligent systems, 2017, 12(1): 47–54
[6] 郭伦众, 宋振明. 一种基于最大满矩阵生成概念格的算法[J]. 智能系统学报, 2015, 10(6): 838–842
GUO Lunzhong, SONG Zhenming. A novel concept-lattice acquisition approach based on the greatest full matrix of formal context[J]. CAAI transactions on intelligent systems, 2015, 10(6): 838–842
[7] 侯小丰. 形而上学自由概念的生成与终结[J]. 学术研究, 2015(9): 12–19,36
HOU Xiaofeng. The generation and end of the concept of metaphysical freedom[J]. Academic research, 2015(9): 12–19,36
[8] 李进金, 张燕兰, 吴伟志, 等. 形式背景与协调决策形式背景属性约简与概念格生成[J]. 计算机学报, 2014, 37(8): 1768–1774
LI Jinjin, ZHANG Yanlan, WU Weizhi, et al. Attribute reduction for formal context and consistent decision formal context and concept lattice generation[J]. Chinese journal of computers, 2014, 37(8): 1768–1774
[9] 田杰. 基于信息–知识–智能转化律视角的情报概念研究[J]. 情报杂志, 2013, 32(6): 5–9
TIAN Jie. On the concept of intelligence: an information-knowledge-wisdom transformation law perspective[J]. Journal of intelligence, 2013, 32(6): 5–9
[10] 王旭阳, 萧波. 基于概念关联度的智能检索研究[J]. 计算机工程与设计, 2013, 34(4): 1415–1419
WANG Xuyang, XIAO Bo. Research of intelligent retrieval based on concept correlation degrees[J]. Computer engineering and design, 2013, 34(4): 1415–1419
[11] 张家精, 王焕宝, 倪友聪, 等. 云模型的隶属概念判定中阈值生成[J]. 计算机工程与应用, 2011, 47(24): 125–128
ZHANG Jiajing, WANG Huanbao, NI Youcong, et al. Generating thresholds to determine membership concepts based on cloud model[J]. Computer engineering and applications, 2011, 47(24): 125–128
[12] 孙福振, 李贞双. 概念语义生成与文本特征选择研究[J]. 计算机工程与应用, 2011, 47(30): 116–118
SUN Fuzhen, LI Zhenshuang. Research on concept semantic space and text feature selection[J]. Computer engineering and applications, 2011, 47(30): 116–118
[13] 刘海生, 齐铁力. 基于可拓智能体的概念设计自动化求解方法[J]. 机械设计与制造, 2010(6): 221–223
LIU Haisheng, QI Tieli. An extenic-agent based autodesign strategy for mechanical product conceptual design[J]. Machinery design & manufacture, 2010(6): 221–223
[14] 崔铁军, 李莎莎. 系统故障因果关系分析的智能驱动方式研究[J]. 智能系统学报, 2021, 16(1): 92?97.
CUI Tiejun, LI Shasha. Intelligent analysis of system fault data and fault causal relationship[J]. CAAI transactions on intelligent systems, 2021, 16(1): 92?97.
[15] 崔铁军, 李莎莎. 系统可靠–失效模型的哲学意义与智能实现[J]. 智能系统学报, 2020, 15(6): 1104–1112
CUI Tiejun, LI Shasha. Philosophical significance of system reliability-failure model and intelligent implementation[J]. CAAI transactions on intelligent systems, 2020, 15(6): 1104–1112
[16] 崔铁军, 李莎莎. SFEP文本因果关系提取及其与SFN转化研究[J]. 智能系统学报, 2020, 15(5): 998–1005
CUI Tiejun, LI Shasha. Causality extraction of SFEP text and its conversion to SFN[J]. CAAI transactions on intelligent systems, 2020, 15(5): 998–1005
[17] 崔铁军, 李莎莎. 人工智能系统故障分析原理研究[J]. 智能系统学报, 2021, 16(4): 785?791.
CUI Tiejun, LI Shasha. Research on system fault analysis principle based on artificial intelligence system[J]. CAAI transactions on intelligent systems, 2021, 16(4): 785?791.
[18] CUI Tiejun, WANG Peizhuang, LI Shasha. Research on uncertainty of system function state from factors-data-cognition[J]. Annals of data science, 2022, 9(3): 593–609.
[19] CUI Tiejun, LI Shasha. Recognition and determination of fuzzy logical relationship in the system fault evolution process[J]. Information processing and management, 2021, 58(5)–102630.
[20] CUI Tiejun, LI Shasha. System movement space and system mapping theory for reliability of IoT[J]. Future generation computer systems, 2020, 107: 70–81.
[21] CUI Tiejun, LI Shasha. Logic expression and structure simplification for SFEP of sensor system[J]. IEEE sensors journal, 2021, 21(22): 25214–25221.
[22] 钟义信, 张瑞. 信息生态学与语义信息论[J]. 图书情报知识, 2017(6): 4–11
Zhong Yixin, Zhang RUI. Information ecology and semantic information theory[J]. Documentation, information & knowledge, 2017(6): 4–11
[23] 钟义信. 从“机械还原方法论”到“信息生态方法论”——人工智能理论源头创新的成功路[J]. 哲学分析, 2017, 8(5): 133–144,199
Zhong Yixin. From mechanical reductionism to methodology of information ecology: successful approach to innovation for AI theory[J]. Philosophical analysis, 2017, 8(5): 133–144,199
[24] 钟义信. 从信息科学视角看《信息哲学》[J]. 哲学分析, 2015, 6(1): 17–31,197
Zhong Yixin. Information science and Its view on Information Philosophy[J]. Philosophical analysis, 2015, 6(1): 17–31,197
[25] 钟义信. 高等智能·机制主义·信息转换[J]. 北京邮电大学学报, 2010, 33(1): 1–6
Zhong Yixin. Advanced intelligence-mechanjsm approach-infomation conversion[J]. Journal of Beijing University of Posts and Telecommunications, 2010, 33(1): 1–6
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