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．     

类比转换原理及其实现

类比转换完成将已知情况（称基）的知识引入到相似新情况（称靶），从而求解靶或学习到关于靶的新知识．本文的类比转换原理讨论了如何选择最佳映射，怎样在靶中创建对象和谓词，以及转换基中那些命题到靶．并基于该原理设计了类比转换的计算模型，实现了类比转换器ＡＴＥ．实例分析表明ＡＴＥ生成的类比结论既具创造性，又有较高可信度．     

一种随机类比推理模型的研究与实现

近年来,联想类比推理的研究得到了许多人的关注,并且取得了很多成果.但是在该领域中还存在很多有待探索的间题。基于此,我们提出了随机类比联想原理,随机类比可联想原理,随机高概类比可联想原理,不     

预测系统中的类比推理方法

本文提出用类比方法处理历史性数据的模型。该模型将所有与“目标”系统相似的“源”系统分别进行类比处理、再综合所有类比结果、得到预报策略。并且,给出了该模型的实现算法。     

联想思维的超拓扑结构模型

从人类实际的知识表示结构出发来研究联想思维的本质规律，提出了超拓扑结构联想模型，给出了结合处理相似相关与对立相关的联想计算方法和分层优先的联想确认算法．事实证明，该模型的确是人类联想思维的一般化模型。     

业务流程模型相似度研究综述

随着业务流程模型管理库规模的增大，传统的模型管理方式在效率和准确度方面已经无法达到预期，研究能够提升业务流程模型管理效率的技术成为人们的迫切需求。其中，业务流程模型相似度技术在模型搜索、模型一致性检测等模型管理的相关应用场景中能够有效提升工作的效率和精度，因此，对业务流程模型相似度技术的研究已经逐渐成为模型分析领域的一个研究热点，并取得了许多有价值的研究成果。业务流程模型相似度技术涉及的领域较多，可以向不同的分支方向发展，虽然不同分支的模型相似度技术会有方法之间的类比，但是缺乏系统性的整理和分析。文中从相似度计算方法和应用场景这两个层面对业务流程模型相似度技术进行了分类讨论，将相似度计算方法分为文本相似度、语义相似度、结构相似度、行为相似度和基于人类评估的相似度，并分析了每种计算方法的特点。较为常见的业务流程模型相似度应用场景包括一致性检测、标准化、流程模型搜索和模型重用，文中对基于以上场景的相关研究进行了梳理。最后分析了业务流程模型相似度研究面临的挑战。     

联想的空间模型

本文讨论了联想的认知概念,归纳出了联想的一般性原理。通过开放对象之间的相似方式,构造了它的度量空间模型,并进一步诱导出更一般的拓扑空间模型,定义了距离、邻域等空间模型,同时得出一些重要的结论。     

范例推理在地理信息系统中的应用研究

范例推理（CBR）是一种用先前求解问题的经验和方法，通过类比和联想来解决当前相似问题的推理技术，它是动态决策环境下求解不良结构问题的常用方法。GIS系统作为一种新兴的地学工具，具有很强的空间分析能力，但由于地学问题的复杂性，一些地学现象很难用确切的模型进行模拟和预测。考虑到范例推理系统在处理半结构化和非结构化问题方面的出色能力，文中探讨了一个基于范例推理的GIS系统结构，并给出了地理范例的构建方法和表达模型。     

卫星控制系统全物理仿真

仿真是应用相似定理和类比关系来研究事物,也就是用模型代替实际系统进行实验和研究.在卫星控制系统仿真中,根据所介入的模型的不同,分为数学仿真和物理仿真两类.卫星控制系统全物理仿真是研制卫星过程中特有的一种仿真方法,它利用气浮台作为运动模拟器,又称气浮台仿真.该文主要叙述卫星控制系统全物理仿真的原理和用途,简要介绍单轴气浮台和三轴气浮台两个全物理仿真系统的组成、主要设备的技术指标以及卫星控制系统全物理仿真的几个实例.     

一种改进的类比估算方法及案例研究

在软件成本估算中类比估算所要解决的主要问题之一是如何定义合适的相似度,有必要充分利用模型信息提高估算精度.提出一种改进的类比估算方法,定义模型属性信息空间上由第一类曲线积分计算的弧长为相似度.在此基础上开发了改进的类比估算工具ImpAnalogy,并且进行了案例研究.结果表明改进后的类比估算方法有效地提高了估算精度.     

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．     

A parallel algorithm for the eight‐puzzle problem using analogical reasoning

The emphasis in this article will be on people's analogical use of fairly explicit pieces of knowledge in problem solving situations. Analogical reasoning (AR) has long been recognized as an important component of problem solving. In general, AR involves using the (possibly modified) solution of one problem to solve another. The preconditions that need to be satisfied in order to apply AR are to locate the common features that are shared among the two problems. The case study considered here is the eight‐puzzle problem. The underlying learning is a three‐stage process. First, is the building stage, where heuristic search techniques are used to find solutions to a set of initial puzzles. The next stage, the reminding stage, identifies a group of solutions with similar properties to the input puzzle (supposedly, this puzzle is solvable). During the final stage, the analogical transformation stage, many movement operators are applied to similar solutions already extracted from the reminding stage, in order to find the solution to the original (input) puzzle. One of the major drawbacks is that AR requires a lot of computational power for global searching before it can be converged. Therefore, in order to outperform heuristic algorithms, a parallel version of AR is introduced. Parallel computation is focused on the last two stages of AR. A design and analysis of parallel algorithms of the reminding stage and the analogical transformation stage are presented. © 2002 Wiley Periodicals, Inc.     

类比推理系统BHARS

类比推理提供了一种新的问题求解机制和机器学习方法，本文的类比推理系统ＢＨＡＲＳ包括四个处理部件，即联想器、求精器、匹配器和转换器，文章详细介绍了这四个处理部件的工作原理，并以实例说明了它们的工作过程，与同类系统相比，ＢＨＡＲＳ有功能全面、适应面广和执行效率高的特点。     

Analogical proportions

Analogy-making is at the core of human and artificial intelligence and creativity with applications to such diverse tasks as proving mathematical theorems and building mathematical theories, common sense reasoning, learning, language acquisition, and story telling. This paper introduces from first principles an abstract algebraic framework of analogical proportions of the form ‘a is to b what c is to d’ in the general setting of universal algebra. This enables us to compare mathematical objects possibly across different domains in a uniform way which is crucial for AI-systems. It turns out that our notion of analogical proportions has appealing mathematical properties. As we construct our model from first principles using only elementary concepts of universal algebra, and since our model questions some basic properties of analogical proportions presupposed in the literature, to convince the reader of the plausibility of our model we show that it can be naturally embedded into first-order logic via model-theoretic types and prove from that perspective that analogical proportions are compatible with structure-preserving mappings. This provides conceptual evidence for its applicability. In a broader sense, this paper is a first step towards a theory of analogical reasoning and learning systems with potential applications to fundamental AI-problems like common sense reasoning and computational learning and creativity.

     

基于对立的联想计算

本文讨论了联想的认知概念,归纳出联想的一般性原理,并在此基础上给出了基于对立的联想计算理论,其中包括对立概念,对立度的计算规则,基于对立度的联想规则。同时还讨论了模拟退火判定算法。使得对立度低于阈值的靶仍然能以较小的概率被想起。这正确地反映了联想思想的创造性,克服了已有模型的不足。     

Towards understanding and supporting creativity in design: analogies in a constrained cognitive environment

In order to show that the emergence of new ideas takes place in a “constrained cognitive environment”, we conducted two experimental studies in a creative professional area: non-routine design. The first study is focused on the role of analogical reasoning in creativity and, especially, on the nature of potential “sources” of inspiration, which facilitate the evocation process. The second study aims at understanding on which ground designers of different levels of expertise construct their own constrained cognitive environment. Based on the obtained results, we suggest ways to facilitate creative acts from designers.     

Analogical reuse of requirements specifications: A computational model

Specifications of requirements for new software systems can be revised, refined, or completed in reference to specifications of requirements for existing similar systems. Although realized as a form of analogical problem solving, specification by reuse is not adequately supported by available computational models for detecting analogies. This is chiefly due to the following reasons: (1) It is assumed that specifications are expressed according to the same specification model and in a uniform representation scheme. (2) Additional information is needed for the detection of analogies, which is not contained in the specifications. (3) Performance scales poorly with the complexity of specifications. This article presents a computational model for detecting analogies, which addresses these issues to a certain extent. The application of the model in the specification of requirements by analogical reuse is demonstrated through an example, and its sensitivity to the representation of specifications is discussed. Finally, the results of a preliminary empirical evaluation of the model are reported.     

An AI model of case-based legal argument from a jurisprudential viewpoint

This article describes recent jurisprudential accountsof analogical legal reasoning andcompares them in detail to the computational modelof case-based legal argument inCATO. The jurisprudential models provide a theoryof relevance based on low-levellegal principles generated in a process ofcase-comparing reflective adjustment. Thejurisprudential critique focuses on the problemsof assigning weights to competingprinciples and dealing with erroneously decidedprecedents. CATO, a computerizedinstructional environment, employs ArtificialIntelligence techniques to teach lawstudents how to make basic legal argumentswith cases. The computational modelhelps students test legal hypotheses againsta database of legal cases, draws analogiesto problem scenarios from the database, andcomposes arguments by analogy with a setof argument moves. The CATO model accountsfor a number of the important featuresof the jurisprudential accounts, includingimplementing a kind of reflective adjustment.It also avoids some of the problems identifiedin the critique; for instance, it deals withweights in a non-numeric, context-sensitivemanner. The article concludes by describingthe contributions AI research can make tojurisprudential investigations of complexcognitive phenomena of legal reasoning. Forinstance, unlike the jurisprudential models,CATO provides a detailed account of how togenerate multiple interpretations of a citedcase, downplaying or emphasizing the legalsignificance of distinctions in terms of thepurposes of the law as the argument contextdemands.