基于前向推理的平面解析几何自动推理系统研究与实现

采用传统的前向推理策略，使用了代数和几何相结合的推理方法，把解方程作为核心思想，较完善地实现了平面解析几何的自动推理系统。实践证明，本系统可以对大部分初等平面解析几何问题进行自动解题。     

双向推理系统在初等几何自动解题中的实现*

目前采用较多的初等几何自动推理方法是单向推理法（包括前向推理和后向推理）,探讨将前推法与后推法结合起来进行初等几何的推理。实践证明,对较为复杂的几何问题来说,采用这种方法可以显著提高推理效率。     

SGARP中符号计算模块的实现及其应用

可持续发展的几何自动推理平台(sustainable geometry automated reasoning platform, SGARP)支持用户按需添加或修改几何定理机器证明所涉及的几何对象、谓词、定理和规则,以发展多种多样基于规则的机器自动推理或人机交互推理方法.为进一步提高SGARP的推理能力和扩展其适用范围,提出一种在SGARP中实现符号计算功能的快捷方法,并成功添加了质点法和解析法推理模块.质点法可证明希尔伯特交点类几何命题,解析法能用于辅助证明各种类型有一定难度的几何定理,如著名的Thebault定理.对这两种方法用基于Web的机器证明测试用的几何问题库(thousands of geometric problems for geometric theorem provers, TGTP)中180道几何题进行评估,均在合理时间内给出令人满意的可读机器证明,表明升级后的SGARP能更好地满足用户学习与发展几何机器推理的需求.     

A geometry modeling system for ray tracing or particle transport Monte Carlo simulation

The principles of a geometry modeling system for ray tracing or Monte Carlo particle transport simulation are presented. The model uses the boundary representation of volumes and was developed using object oriented programming. The surface is adopted as the basic element in the model. Complex structures are described using the derived concept of compound surfaces. Particle tracking exceptions caused by floating point rounding errors are discussed and robust algorithms that use geometrical reasoning to address these problems are presented.     

A knowledge-based program for diagnosing metallurgical failures

Failure analysis of structures, a component of the design process, attempts to minimize the costs of mechanical failure by recommending changes in the geometry, materials, processing, or servicing of critical items in order to increase structural reliability. This paper presents a model of failure analysis reasoning and demonstrates techniques of artificial intelligence (AI) that use this model to solve difficult problems of data interpretation. Experiences with a rule-based expert system are used to illustrate problems of knowledge organization and management as the domain of the program gets very large.     

基于约束参数化的设计技术研究现状分析

系统分析了基于约束的参数化设计技术的研究现状。首先指出早期CAD系统初级参数化的主要缺陷；根据参数化设计方法在不同时期的主要特点，回顾了参数化设计的发展过程。参数化方法被划分为程序参数化、在线交互参数化和基于自组织方式的离线参数化。在在线交互参数化方法中，重点探讨了初等方法、变量几何法、人工智能法和构造过程法的原理和特点，并在人工智能法中介绍了基于知识的几何推量法、基于自由度分析的约束传播法和基于神经网络的自学习法；给出基于自组织方式离线参数化方法的主要步骤和特点。最后阐明了各种参数化方法存在的问题，以及该技术将来的发展方向。     

Overcoming rule-based rigidity and connectionist limitations through massively-parallel case-based reasoning

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificial intelligence presents major problems as well. We claim that a promising way out of this impasse is to build neural net models that accomplish massively parallel case-based reasoning. Case-based reasoning, which has received much attention recently, is essentially the same as analogy-based reasoning, and avoids many of the problems leveled at traditional artificial intelligence. Further problems are avoided by doing many strands of case-based reasoning in parallel, and by implementing the whole system as a neural net. In addition, such a system provides an approach to some aspects of the problems of noise, uncertainty and novelty in reasoning systems. We are accordingly modifying our current neural net system (Conposit), which performs standard rule-based reasoning, into a massively parallel case-based reasoning version.     

基于消点法的几何自动推理系统实现

为了实现几何自动推理的可读性证明,并提高推理效率,介绍了一个基于消点法的可构造性几何命题自动推理系统的设计与实现。该系统提供作图的方式接受用户的几何命题前提条件的输入,可以对初等几何中的大部分可构造性几何问题进行自动证明和求解,并生成可读的证明步骤,大大方便了初高等几何教育和相关研究者的需要。     

可由用户持续发展的几何自动推理平台的推理算法

目前的几何定理证明器都不具有可持续性。提出一种结构具有一般性的知识表示和能够统一处理所有规则的推理算法，初步实现了可由用户持续发展的几何自动推理平台。该推理平台允许用户添加几何知识，如几何对象、谓词和规则，并可以综合使用多种推理算法，如前推搜索法和一部分面积法，它将更适合用于几何教学。     

The problem of finding a restriction strategy more effective than the set of support strategy

This article is the seventeenth of a series of articles discussing various open research problems in automated reasoning. Here we focus on formulating a new restriction strategy for curtailing the number of clauses generated and retained during the completion of an assignment. The problem proposed for research asks one to find a strategy that sharply restricts the tendency of an automated reasoning program to deduce far too many conclusions, many of which are redundant or irrelevant. The new strategy must be more powerful than the set of support strategy on a substantial set of problems. We suggest possible test problems from Tarskian geometry and the equivalential calculus for evaluating a proposed solution to this research problem.This work was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

Data classification through an evolutionary approach based on multiple criteria

Real-world problems usually present a huge volume of imprecise data. These types of problems may challenge case-based reasoning systems because the knowledge extracted from data is used to identify analogies and solve new problems. Many authors have focused on organizing case memory in patterns to minimize the computational burden and deal with uncertainty. The organization is usually determined by a single criterion, but in some problems, a single criterion can be insufficient to find accurate clusters. This work describes an approach to organize the case memory in patterns based on multiple criteria. This new approach uses the searching capabilities of multiobjective evolutionary algorithms to build a Pareto set of solutions, where each one is a possible organization based on the relevance of objectives. The system shows promising capabilities when it is compared with a successful system based on self-organizing maps. Due to the data set geometry influences, the clustering building process results are analyzed taking into account it. For this reason, some complexity measures are used to categorize data sets according to their topology.     

基于J2EE的桥梁协同设计模型

协同设计是工程设计发展的必然趋势,针对桥梁设计对协同工作的需求,把协同设计思想与桥梁CAD系统结合起来,提出基于J2EE平台的桥梁协同设计系统,包括桥梁协同设计的数据表达模型、基于几何约束的推理算法和基于设计约束的推理算法,以桥梁抗震设计群体、结构设计群体和地震动信息采集群体的协同工作为例验证系统的可实施性。     

基于多层推理机制的机械产品概念设计

将类比推理的方法与基于实例的方法相结合，建立了基于多层推理的机械产品概念设计系统、知识的表示，采用面向应用的规则方法，框架式的知识结构。该系统有自学习的能力，也可以由用户进行知识的更改和完善，用户界面良好。     

时空推理研究进展

与时态和空间有关的推理问题是人工智能研究中重要的组成部分,在地理信息系统、时空数据库、CAD/CAM等领域有着重要应用.从本体、表示模型和推理方法3个方面分别介绍了时态推理和空间推理的发展,并在此基础上综述了时空结合推理的研究进展.讨论了目前时空推理领域存在的问题,并指出了今后的发展方向.     

多值逻辑系统W_n中α-三I问题的形式解

在多值逻辑系统Wn中提出了琢-三I问题。通过在F(S)中引入一个偏序,讨论了琢-GMP和多重琢-GMP问题的形式化推理问题,得到了琢-FMP问题的形式化三I解,从而在多值逻辑系统Wn中建立了琢-三I问题的形式化推理机制。     

基于CBR与RBR混合推理的仪表设计专家系统

针对仪表设计领域知识和经验的重用和共享性差的问题，完成了仪表设计专家系统的开发。对该系统的功能模型作了详细的阐述，并对知识库的设计和推理机制进行了深入研究，提出了一种将CBR与RBR相结合的新型推理方法，该方法结合两种推理方式的优势，提高了推理的质量和效率。实验表明，该系统能够根据用户的要求进行仪表设计，具有广泛的应用前景。     

GHCAD: A Feature-based Design System

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几何信息压缩在计算机自动推理系统中的研究

着力于在推理系统中合理地组织几何信息的数据结构,提高推理的效率,减小算法的时间复杂度,对信息进行了描述,采用了等价类、角的压缩和归一化、信息组对等方法,阐述了几何信息在推理时压缩的实现,介绍了压缩信息的展开与提取技术,并通过计算机程序证明该类方法可以实现。实验结果表明,对较复杂的几何问题来说,采用这种方法可以显著提高推理效率。     

基于复数法的几何定理可读机器证明

已有的机器证明方法在处理一些涉及大规模符号运算的几何问题时,常因算法复杂度过高或机器能力的限制,有时并不能在合理时间内实现可读机器证明. 故提出了复数法这一新的几何定理机器证明算法,并选用符号计算功能较为强大的软件Mathematica创建了新证明器CNMP(complex number method prover).新提出的复数法能有效地解决构造型几何命题,对用于测试与评价几何定理证明器性能的综合性平台TGTP(thousands of geometric problems for geometric theorem provers)上的180个几何问题的实验结果表明,CNMP的解题能力与运行效率均令人满意.尤其是对于一些具有相当难度的几何定理,如五圆定理、Morley定理、Lemoine圆定理、Thebault定理、Brocard圆定理等,CNMP均能在短时间内给出可读机器证明.     

基于本体的应急救援知识库构建及其应用

针对专家系统在应急救援领域应用中存在的知识表示及推理等问题,采用基于本体的知识表示方法与基于Jena的规则推理引擎,参考简单知识工程方法论与Jena规则语法建立一个高速公路应急救援本体与推理规则,实现本体知识库的推理。将该知识库应用于高速公路应急救援系统中,结果表明其具备解决实际问题的能力;有利于领域知识的共享与重用;促进了专家系统在高速公路应急救援领域的发展。