一种类比匹配原理及其实现

本文给出了一种类比匹配原理及其计算模型，并在此基础上实现了类比匹配器ＡＭＥ．其中类比匹配原理包括一致性、谓词相似和支持性原则；计算模型采用以命题为中心，以命题映射为基本单位，从而ＡＭＥ的复杂性低于同类系统．另外，证明了本文的实现算法具完备性和正确性．     

发展中的欧洲气象卫星计划

发展中的欧洲气象卫星计划盛永伟国家卫星气象中心国家卫星气象中心十分重视同国际伙伴的合作，已同ＮＯＡＡ（美国海洋与大气管理局）和ＥＵＭＥＴＳＡＴ（欧洲气象卫星组织）等建立了良好的关系。应ＥＵ－ＭＥＴＳＡＴ主任Ｊ．Ｍｏｒｇａｎ博士的邀请，笔者有幸于１９９...     

基于类比的学习式搜索算法AMO.GLSA

本文首先给出了学习式搜索的一个问题模型，然后（５）中ＧＬＳ搜索解题系统的基础上，本文描述了一个多目标学习搜索算法ＭＯ．ＧＬＳＡ，并对该算法作出一性能评价，最后，文中给出了一个基于类比的学习搜索算法ＡＭＯ．ＧＬＳＡ。     

基于多源类比生成的知识表达模型

本文提出了一个多源类比生成的模型，将传统的类比从二值符号扩展到连续空间，使类比从二类比源发展成为多源多部件类比，并将传统的类比由问题求解式改造成生成类比，这对设计领域的创造性工作意义重大，在此基础上实现了国家“８５”科技攻关“彩色拼版制版ＣＡＤ／ＣＡＭ：ＡＤＳ２．０”实验系统，实验结果证实了该模型的有效性。     

集成CASE的集成模型

本文综述计算机辅助软件工程ＣＡＳＥ概念的发展，从第一代文件系统集成的ＵＮＩＸ环境到９０年代初的基于仓库的ＣＡＳＥ环境．集成技术方面始终围绕数据集成、控制集成、表达集成发展．ＣＡＳＥ的集成还要保证各厂家开发的工具和可重用成分可移植．为此，本文介绍了近代ＣＡＳＥ环境集成的模式，ＣＡＩＳ和ＰＣＴＥ作为集成核心模型的原理，以及发展到８０年代后期信息仓库的由来．信息仓库是ＰＣＴＥ成果的进一步发展，１９８８年ＥＣＭＡ提出以信息仓库为核心的烤面包实现集成模型．它是当今ＣＡＳＥ环境研究开发的基本模型．本文详细论述了信息仓     

电子商务技术漫谈(之六) 电子商务的CA/SET认证体系

ＣＡ／ＳＥＴ（Ｓｅｃｕｒｅ Ｅｌｅｃｔｒｏｎｉｃ Ｔｒａｎｓａｃｔｉｏｎ安全电子交 易）安全认证体系通常用于保障涉及到银行支付卡的电子商务业务的安全,其实现应遵循安全电子交易协议ＳＥＴ的规定,本专题将简要介绍有关ＣＡ／ＳＥＴ安全认证体系的相关内容和构想,如果读者想了解相关的详细内容请参照ＳＥＴ协议。 ＣＡ／ＳＥＴ的分级结构 ＣＡ／ＳＥＴ的分级结构如图１所示： ＣＡ／ＳＥＴ安全认证体系的结构依据ＳＥＴ标准和实际情况通常可分为四级：第一级为ＳＥＴ根ＣＡ（ＲＣＡ：Ｒｏｏｔ ＣＡ）,第二级为 ＳＥＴ品牌 ＣＡ（ …     

浙江省干部计算机应用能力考试推介（二）

浙江省干部计算机应用能力考试推介（二）模拟试题一：ＤＯＳ操作题１在当前考生目录下，先建立一个名为ＴＥＭＰ的新子目录，并将当前考生目录下的所有文件复制到ＴＥＭＰ子目录中。２用ＤＩＲ命令列出ＴＥＭＰ子目录下文件；井将ＳＴ．ＷＰＳ文件名改为ＳＴＢＡＫ．ＷＰ...     

基于相关性的类比求精

本文提出了一种基于相关性的类比求精方法，该方法能够在相似情况中自动寻找与当前类比有关的知识。     

兴业国际PANNET！商用网络中心

兴业国际ＰＡＮＮＥＴ！商用网络中心ＰＡＮＮＥＴ提供的产品及服务产品内容服务项目内容ＩＮＴＥＲＮＥＴ基本服务·电子邮件传送（Ｅ－ｍａｉｌ）·联机检索（Ｇｏ－ｐｈｅｒ，Ａｒｃｈｉｅ）·信息及文本传输（ＦＴＰ）·信息查询（ＷＷＷ）·远程登录（Ｔｅｌｎｅｔ）...     

AN-2000:一种新型的带V5.2接口的综合接入系统

ＡＮ－２０００：一种新型的带Ｖ５．２接口的综合接入系统●祝斐一、接入网及Ｖ５接口发展历史连接本地交换局（ＬＥ）和用户终端设备（ＴＥ）的用户线（或称用户环路），无论从实现业务接入或是占通信投资比重方面都占重要地位。长期以来，世界各国的通信网关系到国家命...     

APA：类比自动程序设计系统

自动程序设计系统APA使用类比推理技术,向过去经验学习编写新的LISP程序.本文首先给出了它的知识表示和系统结构,然后讨论怎样发现与新问题相似的已解问题,怎样沿着相似推导构作新问题的程序.APA是[1,2]中类比推理理论的一个实验系统.     

Analogical model formulation for transfer learning in AP Physics

Transfer learning is the ability to apply previously learned knowledge to new problems or domains. In qualitative reasoning, model formulation is the process of moving from the unruly, broad set of concepts used in everyday life to a concise, formal vocabulary of abstractions, assumptions, causal relationships, and models that support problem-solving. Approaching transfer learning from a model formulation perspective, we found that analogy with examples can be used to learn how to solve AP Physics style problems. We call this process analogical model formulation and implement it in the Companion cognitive architecture. A Companion begins with some basic mathematical skills, a broad common sense ontology, and some qualitative mechanics, but no equations. The Companion uses worked solutions, explanations of example problems at the level of detail appearing in textbooks, to learn what equations are relevant, how to use them, and the assumptions necessary to solve physics problems. We present an experiment, conducted by the Educational Testing Service, demonstrating that analogical model formulation enables a Companion to learn to solve AP Physics style problems. Across six different variations of relationships between base and target problems, or transfer levels, a Companion exhibited a 63% improvement in initial performance. While already a significant result, we describe an in-depth analysis of this experiment to pinpoint the causes of failures. Interestingly, the sources of failures were primarily due to errors in the externally generated problem and worked solution representations as well as some domain-specific problem-solving strategies, not analogical model formulation. To verify this, we describe a second experiment which was performed after fixing these problems. In this second experiment, a Companion achieved a 95.8% improvement in initial performance due to transfer, which is nearly perfect. We know of no other problem-solving experiments which demonstrate performance of analogical learning over systematic variations of relationships between problems at this scale.     

Semantics in analogical reasoning

Abstract

To have broad application or approach the capacity of ordinary human thinking, analogical reasoning programs must become more complex or their semantics must become richer, or both. Little research is being done to discover what a broad collection of semantics can contribute to general-purpose analogical reasoning. From an analysis of a small collection of words, two classes containing over 300 categories are determined representing the semantics for understanding metaphors. A broad-based collection of simple target is source metaphors is sampled with each target and source represented in terms of these categories without known influence from the respective metaphor. A computational model is developed drawing on several disciplines while using rules for the recognition and elaboration phases of reasoning. Recognition primarily involves finding and reorganizing relevant schemata within the source. Elaboration involves reorganizing and possibly adding to the target. Given each metaphor, the TisS computer program creates a new representation of each target and source including several meanings for each metaphor. A test with human subjects making aptness and agreement judgements on the generated statements is discussed, suggesting a methodology for using a rich semantic base in analogical reasoning.     

Effects of multimedia and schema induced analogical reasoning on science learning

The present study investigates the effects of multimedia and schema induced analogical reasoning on science learning. It involves 89 fourth grade elementary students in the north‐east of the United States. Participants are randomly assigned into four conditions: (a) multimedia with analogy; (b) multimedia without analogy; (c) analogy without multimedia; and (d) non‐multimedia and non‐analogy. The multivariate analyses of covariance reveal significant main effects for multimedia and analogy learning as well as a significant interaction between multimedia and analogy. The findings show that schema induced analogical reasoning can significantly improve science learning and that multimedia becomes more effective when it is integrated with an instructional method such as analogy and less so when it is used only as a visual tool. The study also shows the field dependence/independence as a significant covariate that influences learners' schema induced analogical reasoning in learning. Discussions pertaining to the significance of the findings and their implications for teaching and learning are made. Suggestions for future research are included with an emphasis on developing multimedia supported analogical reasoning for science learning.     

知识迁移的极大熵聚类算法及其在纹理图像分割中的应用

本文研究了一种新型的基于知识迁移的极大熵聚类技术。拟解决两大挑战性问题:1）如何从源域中选择合适的知识对目标域进行迁移学习以最终强化目标域的聚类性能;2）若存在源域聚类数与目标域聚类数不一致的情况时,该如何进行迁移聚类。为此提出一种全新的迁移聚类机制,即基于聚类中心的中心匹配迁移机制。进一步将该机制与经典极大熵聚类算法相融合提出了基于知识迁移的极大熵聚类算法（KT-MEC）。实验表明,在不同迁移场景下的纹理图像分割应用中,KT-MEC算法较很多现有聚类算法具有更高的精确度和抗噪性。     

Model-based design indexing and index learning in engineering design

Design is a wide-ranging and open-ended information-processing task in engineering; analogy is a very common method for addressing engineering design problems. Analogical design is the process of retrieving a known design (source analog) similar to the current design problem (target), and transferring parts of the structure of the known design to the current problem. The power of an analogical designer thus comes in part from the ability to retrieve the “right” design when a new problem is specified. Indexing of designs is therefore an important issue in analogical design. This issue, in fact, has three different aspects: (1) indexing vocabulary, (2) learning of the indices to a new design, and (3) use of the learned indices for retrieving stored designs. The authors of this paper have been exploring the hypothesis that teleologicall causal models of engineering designs give rise to the indexing vocabulary, enable and constrain the learning of indices for new designs, and provide similarity measures for matching a target problem with the stored design and retrieving relevant ones. This hypothesis has been tested in both interactive knowledge acquisition and autonomous design problem solving and learning, in engineering domains such as electrical circuits and heat exchangers.     

类比推理的计算模型

本文将类比推理分为联想、求精、匹配和转换４个过程，在给出各过程遵守的原则之后，详细讨论了它们的计算模型．