基于整数规划的几何因果定性推理建模和求解

因果定性推理是一种通过分析，描述物理系统行为和关系的约束，找出系统内部各个成分之间的因果结构的推理方法，本文提出了一种基于整数 几何因果定性分析模型和算法，该方法在产品设计中有广泛的应用，利用这个模型和算法可以较好地解决参数化设计中的几何推理问题，还可以用作概念设计的工具，用于完成复杂系统设计任务的划分以及定序、设计变量之间相互依赖关系分析等工作，算法具有约束处理能力强、应用范围广、求解效率和稳定     

基于几何推理的装配序列自动规划研究

以问题规约的求解策略和分解法规划产生的装配序列,提出与或图表达装配体拆卸序列解空间,通过求解拆卸序列与或图的解空间,得到装配体的拆卸序列;将拆卸序列反向得到装配序列,为降低装配体拓扑联接图的复杂度,以作业组件做出识别,文中提出判断零件可拆卸性的三个条件,使大部分零件无需进行干涉检验,而由逻辑推理即可判断是否满足拆卸条件,避免了多次试凑,有更高求解效率,可更广泛地用于装配序列求解上。     

新一代几何造型系统数据结构的研究与实现

本文分析了国际上最有影响的２种几何造型数据结构：翼边结构和辐射边结构，并提出了一咱新的数据结构邻维循环结构，邻维循环与辐射边结构一样，具有统一表示线框，表面和实体模型的能力，可以表示点有任意条邻边，边有任意个邻面的形体模型，在拓扑结构部分，邻维循环结构所占存储空间比辐射边结构的对应部分要小一半左右，而在各种拓扑关系的检索效率上，两者的效率相同。     

非流形几何建模拓扑数据结构的研究

当前ＣＡＤ技术的发展趋势是特征造型、行为建模、信息集成。几何建模作为ＣＡＤ系统的核心,是影响ＣＡＤ系统功能的重要因素。非流形几何建模可以用统一的数据结构来表示线框、曲面、实体模型,从而为在底层实现特征造型、变量化设计、行为建模提供了可能。本文概括了非流形几何建模的基础理论知识,提出了一种基于非流形几何建模的新型拓扑数据结构,应用ＯＯＰ及开放式软件开发思想探讨了开发具有自主知识产权的三维实体ＣＡＤ平     

动态识别三维几何约束冲突的方法研究

基于装配几何特征的广义几何约束图,避免了传统几何约束图的超图性质和模糊性,为几何约束满足问题提供了一个清晰的分析模型。文中以此模型来分析产生约束冲突的原因。空间分析法定义和推导了约束满足空间约束满足条件,提出了自由空间和自由度的计算方法,并据此在动态满足三维几何约束的过程中识别约束冲突,明确指出产生约束冲突的原因。     

三维几何约束求解的自由度归约算法

三维几何约束求解在装配设计、几何造型和动力学分析等领域有着广泛的应用．在分析基本几何元素间的约束关系对刚体自由度状态影响的基础上,提出刚体自由度的归约算法,以求得满足约束后刚体的自由度状态空间;以刚体自由度状态空间分析为基础,实现对合理约束的推理求解和约束一致性维护,该算法解决了三维几何约束求解中自由度计算问题,同时避免了一些推理求解算法中出现的“组合爆炸”问题．     

基于归约的几何约束推理研究

参化设计在现代ＣＡＤ技术中占据着越来越重要的地位，几何约束系统的建模、管理与求解是该技术的关键。实践表明，该方法在约束一致性检查、快速求解等方面起了重要的作用。     

人工神经网络在几何造型中的应用

本文介绍了人工神经网络的基本原理和基本特征，对其在几何造型中的应用进行了深入的分析与探讨，提出了基于人工神经网络的曲面光顺，并研究了自由曲面重建和特征识别中人工神经网络的应用方法。     

几何造型设计的NURBS技术

本文在描述非均匀有理Ｂ样条（ＮＵＲＢＳ）基本原理和特点的同时，介绍了一此ＮＵＲＢＳ曲线、曲面造型设计的关键技术。     

基于DFL的缺省假设推理研究

日常生活中人们可以在信息不完全的情况下进行推理并得出较好的推理结论,而且在推理过程中,很多对象都是具有动态模糊性(DF Character)。因此文中针对研究对象以及它们之间的动态模糊性,提出了基于动态模糊逻辑(DFL)的缺省假设推理,并给出了缺省假设推理的框架描述、动态模糊(DF)知识的表示以及推理算法等。     

What Should Default Reasoning be,by Default?

This is a position paper concerning the role of empirical studies of human default reasoning in the formalization of AI theories of default reasoning. We note that AI motivates its theoretical enterprise by reference to human skill at default reasoning, but that the actual research does not make any use of this sort of information and instead relies on intuitions of individual investigators. We discuss two reasons theorists might not consider human performance relevant to formalizing default reasoning: (a) that intuitions are sufficient to describe a model, and (b) that human performance in this arena is irrelevant to a competence model of the phenomenon. We provide arguments against both these reasons. We then bring forward three further considerations against the use of intuitions in this arena: (a) it leads to an unawareness of predicate ambiguity, (b) it presumes an understanding of ordinary language statements of typicality, and (c) it is similar to discredited views in other fields. We advocate empirical investigation of the range of human phenomena that intuitively embody default reasoning. Gathering such information would provide data with which to generate formal default theories and against which to test the claims of proposed theories. Our position is that such data are the very phenomena that default theories are supposed to explain.     

Default Reasoning and Generics

There are numerous logical formalisms capable of drawing conclusions using default rules. Such systems, however, do not normally determine where the default rules come from; i.e., what it is that makes Birds fly a good rule, but Birds drive trucks a bad one.
Generic sentences such as Birds fly are often used informally to describe default rules. I propose to take this characterization seriously, and claim that a default rule is adequate if the corresponding generic sentence is true. Thus, if we know that Tweety is a bird, we may conclude by default that Tweety flies, just in case Birds fly is a true sentence.
In this paper, a quantificational account of the semantics of generic sentences is presented. It is argued that a generic sentence is evaluated not in isolation, but with respect to a set of relevant alternatives. For example, Mammals bear live young is true because among mammals that bear live young, lay eggs, undergo mitosis, or engage in some alternative form of procreation, the majority bear live young. Since male mammals do not procreate in any form, they do not count. Some properties of alternatives are presented, and their interactions with the phenomena of focus and presupposition is investigated.
It is shown how this account of generics can be used to characterize adequate default reasoning systems, and several desirable properties of such systems are proved. The problems of the automatic acquisition of rules from natural language are discussed. Because rules are often explicitly expressed as generics, it is argued that the interpretation of generic sentences plays a crucial role in this endeavor, and it is shown how the theory presented here can facilitate such interpretation.     

Default Reasoning as Situated Monotonic Inference*

Since its inception, situation theory has been concerned with the situated nature of meaning and cognition, a theme which has also recently gained some prominence in Artificial Intelligence. Channel theory is a recently developed framework which builds on concepts introduced in situation theory, in an attempt to provide a general theory of information flow. In particular, the channel theoretic framework offers an account of fallible regularities, regularities which provide enough structure to an agent's environment to support efficient cognitive processing but which are limited in their reliability to specific circumstances. This paper describes how this framework can lead to a different perspective on defeasible reasoning: rather than being seen as reasoning with incomplete information, an agent makes use of a situated regularity, choosing to use the regularity that seems best suited (trading off reliability and simplicity) to the circumstances it happens to find itself in. We present a formal model for this task, based on the channel theoretic framework, and sketch how the model may be used as the basis for a methodology of defeasible situated reasoning, whereby agents reason with simple monotonic regularities but may revise their choice of regularity on learning more about their circumstances.     

Reasoning with Sets of Defaults in Default Logic

We present a general approach for representing and reasoning with sets of defaults in default logic, focusing on reasoning about preferences among sets of defaults. First, we consider how to control the application of a set of defaults so that either all apply (if possible) or none do (if not). From this, an approach to dealing with preferences among sets of default rules is developed. We begin with an ordered default theory , consisting of a standard default theory, but with possible preferences on sets of rules. This theory is transformed into a second, standard default theory wherein the preferences are respected. The approach differs from other work, in that we obtain standard default theories and do not rely on prioritized versions of default logic. In practical terms this means we can immediately use existing default logic theorem provers for an implementation. Also, we directly generate just those extensions containing the most preferred applied rules; in contrast, most previous approaches generate all extensions, then select the most preferred. In a major application of the approach, we show how semimonotonic default theories can be encoded so that reasoning can be carried out at the object level. With this, we can reason about default extensions from within the framework of a standard default logic. Hence one can encode notions such as skeptical and credulous conclusions, and can reason about such conclusions within a single extension.     

若干限制形式的缺省推理的复杂性

该文研究判定一文字是否出现在缺省理论〈D,W〉的某一扩张中的复杂性.其中,D是一集Horn缺省规则,而W是definite Horn公式或者Bi-Horn公式.     

子句型缺省逻辑中的分情形推理

引进一种树型方法以研究缺省逻辑中分情形推理下的Roos扩张,深入讨论了Roos扩张的计算,并分析了Roos扩张与Reiter扩张的关系.为计算Roos扩张,引入了从子句集分解最小文字集的算法.方法对于在缺省逻辑中计算Roos扩张以及分析分情形推理的计算复杂性是有用的.     

Probabilistic Default Reasoning with Conditional Constraints

We present an approach to reasoning from statistical and subjective knowledge, which is based on a combination of probabilistic reasoning from conditional constraints with approaches to default reasoning from conditional knowledge bases. More precisely, we introduce the notions of z-, lexicographic, and conditional entailment for conditional constraints, which are probabilistic generalizations of Pearl's entailment in system Z, Lehmann's lexicographic entailment, and Geffner's conditional entailment, respectively. We show that the new formalisms have nice properties. In particular, they show a similar behavior as reference-class reasoning in a number of uncontroversial examples. The new formalisms, however, also avoid many drawbacks of reference-class reasoning. More precisely, they can handle complex scenarios and even purely probabilistic subjective knowledge as input. Moreover, conclusions are drawn in a global way from all the available knowledge as a whole. We then show that the new formalisms also have nice general nonmonotonic properties. In detail, the new notions of z-, lexicographic, and conditional entailment have similar properties as their classical counterparts. In particular, they all satisfy the rationality postulates proposed by Kraus, Lehmann, and Magidor, and they have some general irrelevance and direct inference properties. Moreover, the new notions of z- and lexicographic entailment satisfy the property of rational monotonicity. Furthermore, the new notions of z-, lexicographic, and conditional entailment are proper generalizations of both their classical counterparts and the classical notion of logical entailment for conditional constraints. Finally, we provide algorithms for reasoning under the new formalisms, and we analyze its computational complexity.     

Case-Based Reasoning in Conceptual Design

The framework for supporting conceptual design using case-based reasoning is studied. Adesign knowledge representation scheme is presented. It is capable of representing both generalized and specific design knowledge. A two-phase case retrieval strategy, called index elaboration, is described which supports both requirement-function transformation and function-feature transformation at conceptual design stage. We propose a prototype system and discuss in detail the realization of supporting conceptual design by CBR.     

A Default Logic Based Framework for Context-Dependent Reasoning with Lexical Knowledge

Lexical knowledge is increasingly important in information systems—for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Information about contexts can be reasoned with as first-order formulae formulae; and (4) Information about contexts can be derived as default inferences. In the dex framework, a word for which lexical knowledge is sought is called a query word. The context for a query word is derived from further words, such as words in the same sentence as the query word. These further words are used with a form of decision tree called a context classification tree to identify which contexts hold for the query word. We show how we can use these contexts in default logic to identify lexical knowledge about the query word such as synonyms, antonyms, specializations, meronyms, and more sophisticated first-order semantic knowledge. We also show how we can use a standard machine learning algorithm to generate context classification trees.