[1]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,(01):37-54.[doi:10.11992/tis.201711034]
 WANG Peizhuang.Factor space-mathematical basis of mechanism based artificial intelligence theory[J].CAAI Transactions on Intelligent Systems,2018,(01):37-54.[doi:10.11992/tis.201711034]
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因素空间理论——机制主义人工智能理论的数学基础(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2018年01期
页码:
37-54
栏目:
出版日期:
2018-01-24

文章信息/Info

Title:
Factor space-mathematical basis of mechanism based artificial intelligence theory
作者:
汪培庄
辽宁工程技术大学 智能工程与数学研究院, 辽宁 阜新 123000
Author(s):
WANG Peizhuang
College of Intelligence Engineering and Mathematics, Liaoning Technical University, Fuxin 123000, China
关键词:
机制主义人工智能理论因素空间理论形式概念分析粗糙集模糊集模糊落影理论背景关系数据挖掘
Keywords:
mechanism-based artificial intelligence theoryfactor space theoryformal concept analysisrough setsfuzzy setsfalling shadow theorybackground relationdatamining
分类号:
TP18
DOI:
10.11992/tis.201711034
摘要:
机制主义人工智能理论是基于智能的生长机制而把结构主义、功能主义和行为主义这三大人工智能流派有机统一起来并使基础意识、情感、理智成为三位一体的高等人工智能理论。因素空间是机制主义人工智能理论的数学基础,是现有模糊集、粗糙集和形式背景理论的进一步提升,它为信息描述提供了一个普适性的坐标框架,把数据变成可视的样本点,形成母体背景分布,压缩为背景基,由此进行概念自动生成,因果关联分析,以及建立在其上的学习、预测、识别、控制、评价和决策等一系列数学操作活动。本文将着重介绍其中的核心内容,将具体的形式信息(即语法信息)与效用信息(即语用信息)关联起来,提升为抽象的语义信息,为机制主义人工智能的信息转化第一定律提供一个简明的数学架构。本文以“九宫棋”为例,介绍如何用因素思维实现目标因素与场景因素的对接和搜索,为信息转化的第二定律从数学上展开探索性的思考;还结合因素空间及有关学科的历史来进行解说,以便帮助读者对因素空间理论有一个较为全面的认识。
Abstract:
Based on using the intelligent growth mechanism, the mechanism-based artificial intelligence theory organically unifies the structure, function, and behaviorism of three genres to form a trinity of consciousness, emotion, and reason. Factor space is the mathematical basis of mechanism-based artificial intelligence theory, which promotes mathematical branches such as formal concept analysis, rough sets, and fuzzy sets, and provides a universal coordinate framework for the description and cognition of things. Data can be represented as visual sampling points in the space and then be cultivated to form the population distribution of the background relation. Based on their relationship, concept generation and causality analysis can be performed automatically, and all rational thinking processes, such as prediction, identification, control, evaluation and decision making, can be performed by factorial algorithms. In this article, we focus on ways to describe formal information (i.e., grammatical information), predict utility information (i.e., pragmatic information) from formal information, and correlate them to generate abstract semantic information, which is helpful for mathematically describing the first established law of information transformation in mechanism-based artificial intelligence theory. We also use factor space theory in chess Tic-Tac-Toe to demonstrate how to dock the target and chess factors, which may provide a clue for how to mathematically describe the second law of information transformation. We also provide a brief history to help readers gain a more comprehensive understanding of the factor space theory.

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备注/Memo

备注/Memo:
收稿日期:2017-11-28。
基金项目:国家自然科学基金委主任基金(61350003); 教育部高校博士学科点专项科研基金资助项目(20102121110002); 辽宁省教育厅科学技术研究一般基金资助项目(L2014133).
作者简介:汪培庄,男,1936年生,教授,博士生导师,主要研究方向为模糊数学及其在人工智能中的应用,近期主要致力于因素空间在人工智能和数据科学中的应用研究。提出和创立了模糊集的随机落影表示、真值流推理和因素空间等数学理论,获得多次国家级和部委级奖励及一次国际奖。出版学术著作4部,发表论文200余篇。
通讯作者:汪培庄.E-mail:peizhuangw@126.com.
更新日期/Last Update: 2018-02-01