[1]崔铁军,李莎莎.基于因素空间的人工智能样本选择策略[J].智能系统学报,2021,16(2):346-352.[doi:10.11992/tis.202003002]
CUI Tiejun,LI Shasha.Sample selection strategy of artificial intelligence based on factor space[J].CAAI Transactions on Intelligent Systems,2021,16(2):346-352.[doi:10.11992/tis.202003002]
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CUI Tiejun,LI Shasha.Sample selection strategy of artificial intelligence based on factor space[J].CAAI Transactions on Intelligent Systems,2021,16(2):346-352.[doi:10.11992/tis.202003002]
基于因素空间的人工智能样本选择策略
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第2期
页码:
346-352
栏目:
学术论文—人工智能基础
出版日期:
2021-03-05
- Title:
- Sample selection strategy of artificial intelligence based on factor space
- Keywords:
- intelligent science; factor space; Moravec’s paradox; sample selection; strategy research; factor driven; strategic network; human thinking
- 分类号:
- TP391;X913;C931.1
- 摘要:
- 为解决人工智能中莫拉维克悖论提出的问题，基于因素空间思想提出一种人工智能样本选择策略。首先通过因素空间论证了莫拉维克悖论的证确定。其次论述了人的选择过程即是比较过程的论断。认为人选择样本需经过三次选择，分别为选择适合的因素、因素概念相和因素量化相，样本空间中样本在这三次选择中逐渐减少最终唯一。最终为实现策略，划分了研究对象，建立了选择策略层次结构，从而建立了人工智能样本选择策略网络模型。实例分析表明：过程中操作基本是因素及因素相的运算，之后才涉及少量的相测量和数据计算。该策略是人对样本的选择过程，也是人工智能样本选择应具备的策略。
- Abstract:
- To solve Moravec’s paradox in artificial intelligence, in this paper, we propose a sample selection strategy based on the factor space. First, we confirm the existence of Moravec’s paradox in the factor space. Then, we determine that the process of human selection is one of comparison. It is considered that human beings must choose samples three times, i.e., they must select suitable factors, a factor concept, and a factor quantification. In the sample space, the number of samples is gradually reduced and finally each is unique after three selection rounds. Finally, to apply this strategy, the research object is divided, and a selection strategy hierarchy is established, thereby establishing the artificial-intelligence sample-selection-strategy network model. Case analysis shows that the actions taken in this process are basically factor and factor-phase operations, in which small amounts of phase measurement and data calculation are involved. This strategy is not only the sample selection process used by human beings, but is also that used by artificial intelligence.
- 参考文献/References:
-
[1] 雷凯, 黄硕康, 方俊杰, 等. 智能生态网络:知识驱动的未来价值互联网基础设施[J]. 应用科学学报, 2020, 38(1):152-172
LEI Kai, HUANG Shuokang, FANG Junjie, et al. Intelligent Eco Networking (IEN):knowledge-driven and value-oriented future internet infrastructure[J]. Journal of applied sciences, 2020, 38(1):152-172
[2] 武春龙, 朱天明, 张鹏, 等. 基于功能模型和层次分析法的智能产品服务系统概念方案构建[J]. 中国机械工程, 2020, 31(n):853-864
WU Chunlong, ZHU Tianming, ZHANG Peng, et al. Conceptual scheme construction of smart pss based on functional model and AHP[J]. China mechanical engineering, 2020, 31(n):853-864
[3] 吴思凡, 杜煜, 徐世杰, 等. 基于深度确定性策略梯度的智能车汇流模型[J]. 计算机工程, 2020, 46(1):87-92
WU Sifan, DU Yu, XU Shijie, et al. Traffic merging model for intelligent vehicle based on deep deterministic policy gradient[J]. Computer engineering, 2020, 46(1):87-92
[4] 刘翠娟, 刘箴, 柴艳杰, 等. 人群应急疏散中一种多智能体情绪感染仿真模型[J]. 计算机辅助设计与图形学学报, 2020, 32(4):660-670
LIU Cuijuan, LIU Zhen, CHAI Yanjie, et al. A multi-agent emotional contagion model in crowd emergency evacuation[J]. Journal of computer-aided design & computer graphics, 2020, 32(4):660-670
[5] 郭健, 陈健, 胡杨. 基于小波智能模型的地铁车站基坑变形时序预测分析[J]. 岩土力学, 2020(S1):299-304
GUO Jian,CHEN Jian,HU Yang. Time series prediction for deformation of the metro foundation pit based on wavelet intelligence model[J]. Rock and soil mechanics, 2020(S1):299-304
[6] 孙伟, 余浩, 杨建平, 等. 智能电网可靠性需求约束下无线发射功率模型预测控制[J]. 电力系统自动化, 2020, 44(2):185-193
SUN Wei, YU Hao, YANG Jianping, et al. Model predictive control of wireless transmit power constrained by reliability requirement of smart grid[J]. Automation of electric power systems, 2020, 44(2):185-193
[7] 李伯虎, 柴旭东, 侯宝存, 等. 云制造系统3.0——一种"智能+"时代的新智能制造系统[J]. 计算机集成制造系统, 2019, 25(12):2997-3012
LI Bohu, CHAI Xudong, HOU Baocun, et al. Cloud manufacturing system 3.0——new intelligent manufacturing system in era of "Intelligence +"[J]. Computer integrated manufacturing systems, 2019, 25(12):2997-3012
[8] 李林恒, 甘婧, 曲栩, 等. 智能网联环境下基于安全势场理论的车辆跟驰模型[J]. 中国公路学报, 2019, 32(12):76-87
LI Linheng, GAN Jing, QU Xu, et al. Car-following model based on safety potential field theory under connected and automated vehicle environment[J]. China journal of highway and transport, 2019, 32(12):76-87
[9] 肖吉军, 郑颖琦, 徐洁萍. 基于AHP与DHNN的智能制造成熟度评估模型研究[J]. 系统科学学报, 2020, 28(2):105-110
XIAO Jijun, ZHENG Yingqi, XU Jieping. Research on intelligent manufacturing maturity evaluation model based on AHP and DHNN[J]. Chinese journal of systems science, 2020, 28(2):105-110
[10] 吴飞, 段书凯, 何斌, 等. 健壮人工智能模型与自主智能系统[J]. 中国科学基金, 2019, 33(6):651-655
WU Fei, DUAN Shukai, HE Bin, et al. Robust artificial intelligence and autonomous intelligent system[J]. Bulletin of national natural science foundation of China, 2019, 33(6):651-655
[11] 毕俊蕾, 朱宗强, 李致远. 智能网联车环境下基于路段评分的数据转发模型[J]. 数据采集与处理, 2019, 34(6):1030-1038
BI Junlei, ZHU Zongqiang, LI Zhiyuan. Road section scoring-based data forwarding model for intelligent connected vehicles[J]. Journal of data acquisition & processing, 2019, 34(6):1030-1038
[12] 钟义信. 机制主义人工智能理论——一种通用的人工智能理论[J]. 智能系统学报, 2018, 13(1):2-18
ZHONG Yixin. Mechanism-based artificial intelligence theory:a universal theory of artifical intelligence[J]. CAAI transactions on intelligent systems, 2018, 13(1):2-18
[13] 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
[14] 钟义信. 人工智能的突破与科学方法的创新[J]. 模式识别与人工智能, 2012, 25(3):456-461
ZHONG Yixin. Breakthroughs in artificial intelligence and innovation in methodology[J]. Pattern recognition and artificial intelligence, 2012, 25(3):456-461
[15] 马立伟. 深度学习驱动的领域专用架构[J]. 中国科学:信息科学, 2019, 49(3):334-341
MA Liwei. Domain-specific architectures driven by deep learning[J]. Scientia sinica informationis, 2019, 49(3):334-341
[16] 汪培庄. 因素空间理论——机制主义人工智能理论的数学基础[J]. 智能系统学报, 2018, 13(1):37-54
WANG Peizhuang. Factor space-mathematical basis of mechanism based artificial intelligence theory[J]. CAAI transactions on intelligent systems, 2018, 13(1):37-54
[17] 汪培庄. 因素空间与数据科学[J]. 辽宁工程技术大学学报(自然科学版), 2015, 34(2):273-280
WANG Peizhuang. Factor spaces and data science[J]. Journal of Liaoning Technical University (Natural Science Edition), 2015, 34(2):273-280
[18] 汪培庄, 郭嗣琮, 包研科, 等. 因素空间中的因素分析法[J]. 辽宁工程技术大学学报(自然科学版), 2014, 33(7):865-870
WANG Peizhuang, GUO Sicong, BAO Yanke, et al. Causality analysis in factor spaces[J]. Journal of Liaoning Technical University (Natural Science Edition), 2014, 33(7):865-870
[19] 崔铁军, 李莎莎. 安全科学中的故障信息转换定律[J/OL]. 智能系统学报.[2020-02-14].http://kns.cnki.net/kcms/detail/23.1538.TP.20191205.1008.002.html.
CUI Tiejun, LI Shasha. The conversion law of fault information in Safety Science[J/OL]. CAAI transactions on intelligent systems.[2019-12-29]. http://kns.cnki.net/kcms/detail/23.1538.TP.20191205.1008.002.html.
[20] 崔铁军, 汪培庄. 空间故障树与因素空间融合的智能可靠性分析方法[J]. 智能系统学报, 2019, 14(5):853-864
CUI Tiejun, WANG Peizhuang. Intelligent reliability analysis method based on space fault tree and factor space[J]. CAAI transactions on intelligent systems, 2019, 14(5):853-864
[21] 崔铁军, 李莎莎. 空间故障树与空间故障网络理论综述[J]. 安全与环境学报, 2019, 19(2):399-405
CUI Tiejun, LI Shasha. Revision of the space fault tree and the space fault network system[J]. Journal of safety and environment, 2019, 19(2):399-405
[22] 崔铁军, 李莎莎, 韩光, 等. 基于信息增益的SFT中故障影响因素降维方法研究[J]. 安全与环境学报, 2018, 18(5):1686-1691
CUI Tiejun, LI Shasha, HAN Guang, et al. On the dimensional reduction method for the fault-affecting factors based on the information gain[J]. Journal of safety and environment, 2018, 18(5):1686-1691
[23] 崔铁军, 李莎莎, 王来贵. 完备与不完备背景关系中蕴含的系统功能结构分析[J]. 计算机科学, 2017, 44(3):268-273, 306
CUI Tiejun, LI Shasha, WANG Laigui. System function structure analysis in complete and incomplete background relationship[J]. Computer science, 2017, 44(3):268-273, 306
[24] 崔铁军, 汪培庄, 马云东. 01SFT中的系统因素结构反分析方法研究[J]. 系统工程理论与实践, 2016, 36(8):2152-2160
CUI Tiejun, WANG Peizhuang, MA Yundong. Inward analysis of system factor structure in 01 space fault tree[J]. Systems engineering-theory & practice, 2016, 36(8):2152-2160
[25] 崔铁军, 马云东. 基于因素空间中属性圆对象分类的相似度研究及应用[J]. 模糊系统与数学, 2015, 29(6):56-63
CUI Teijun, MA Yundong. Research on the similarity of object classification of attribute circular and application based on factors space[J]. Fuzzy systems and mathematics, 2015, 29(6):56-63
[26] 崔铁军, 马云东. 基于因素空间的煤矿安全情况区分方法的研究[J]. 系统工程理论与实践, 2015, 35(11):2891-2897
CUI Tiejun, MA Yundong. Research on the classification method about coal mine safety situation based on the factor space[J]. Systems engineering-theory & practice, 2015, 35(11):2891-2897
[27] MCCULLOCH W S, PITTS W. A logical calculus of the ideas immanent in nervous activity[J]. The bulletin of mathematical biophysics, 1943, 5(4):115-133.
[28] ROSENBLATT F. The perceptron:a probabilistic model for information storage and organization in the brain[J]. Psychological review, 1958, 65(6):386-408.
[29] HOPFIELD J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the national academy of sciences of the United States of America, 1982, 79(8):2554-2558.
[30] MCCARTHY J, MINSKY M L, ROCHESTER N, et al. A proposal for the Dartmouth summer research project on artificial intelligence[R]. Dartmouth:Dartmouth College, 1955.
[31] SIMON H A. The sciences of the artificial[M]. Cambridge, MA, USA:MIT Press, 1969.
[32] NEWELL A. Physical symbol systems[J]. Cognitive science, 1980, 4(2):135-183.
[33] TURING A M. Computing machinery and intelligence[J]. Mind, 1950, 59:433-460.
[34] BROOKS R A. Elephant cannot play chess[J]. Autonomous Robert, 1990, 6:3015.
[35] BROOKS R A. Engineering approach to building complete, intelligent beings[C]//Proceedings of SPIE 1002, Intelligent Robots and Computer Vision VII. Boston, USA, 1989:618-625.
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备注/Memo
收稿日期:2020-03-01。
基金项目:国家自然科学基金资助项目（52004120）；辽宁省教育厅科学研究经费项目（LJ2020QNL018）；国家重点研发计划项目（2017YFC1503102）；辽宁工程技术大学学科创新团队资助项目（LNTU20TD-31）
作者简介:崔铁军，博士，副教授，硕士生导师，主要研究方向为系统可靠性其智能分析理论。提出和建立了空间故障树理论及空间故障网络理论。获得多个期刊优秀论文奖，多个国际SCI期刊、Springer、Science Publishing Group和国内核心期刊审稿专家。出版学术专著7部，申请发明专利23项。发表学术论文近200篇;李莎莎，讲师，博士，主要研究方向为安全管理及其智能分析。出版学术专著2部，申请发明专利5项。发表学术论文20余篇
通讯作者:崔铁军.E-mail:ctj.159@163.com
更新日期/Last Update:
2021-04-25