Automated knowledge acquisition for design and manufacturing: The case of micromachined atomizer

In order to remain competitive in the global market, original equipment manufacturers (OEMs) are developing a process-based, knowledge-driven product development environment with emphasis on the acquisition, storing, and utilization of manufacturing knowledge. This is usually achieved by using the symbolic artificial intelligence (AI) approach. Specifically, knowledge-based expert systems are developed to capture human expertise, mostly in terms of IF–THEN production rules. It has been recognized that the development of symbolic knowledge-based expert systems suffers from the so-called knowledge acquisition bottleneck. Knowledge acquisition is the process of collecting domain knowledge and transforming the knowledge into a computerized representation. It is a challenging and time-consuming process due to the difficulties involved in eliciting knowledge from human experts. This paper presents an automated approach for knowledge acquisition by integrating neural networks learning ability and fuzzy logics structured knowledge representation. Using this approach, knowledge is automatically acquired from data and represented using humanly intelligible fuzzy rules. The approach is applied to a case study of the design and manufacturing of micromachined atomizers for gas turbine engine. The influence of geometric features on the performance of the atomizers is investigated. The results are then compared with those obtained using traditional regression analysis approach (abstract mathematical models). It was found that the automated approach provides an efficient means for knowledge acquisition. Since the fuzzy rules extracted are easy to understand, they can be used to allow more clear specification of manufacturing processes and to shorten learning curves for novice manufacturing engineers.     

UML的形式化及其应用

本文介绍一个面向度量的UML的代数表达框架。这个框架可以作为设计模型检测的基础,并为设计人员提供一个在软件设计进化过程中检测一些设计错误和提出可能的优化方案的理论基础。本文给出了UML类图、序列图和状态图的代数表达并用例子说明了这个UML的代数表达框架的应用及它是如何检测设计错误和提供可能的优化建议的。     

Knowledge representation-oriented nets for discrete event systemapplications

This paper presents knowledge representation-oriented nets (KRON), a knowledge representation schema for discrete event systems (DES). KRON enables the representation and use of a variety of knowledge about a DES static structure and its dynamic states and behavior. It is based on the integration of high-level Petri nets with frame-based representation techniques and follows the object-oriented paradigm. The main objective considered in its definition is to obtain a comprehensive and powerful representation model for data and control of DES. The use of the DES behavioral knowledge is governed by a control mechanism stored in a separate inference engine. KRON provides an efficient execution mechanism to make the models evolve. This is an adaptation of the RETE matching algorithm in order to deal with the features provided by high-level Petri nets and it takes advantage of its integration with a frame/object-oriented representation schema, Moreover, KRON facilitates dealing with decision points in the execution of nondeterministic models. A prototype of a simulation tool with graphical display and animation facilities has been implemented for KRON and it has been used in several case studies in the manufacturing systems domain     

KAL:一个可学习的知识获取环境

可学习的知识获取环境KAL帮助软件工程师从领域专家那儿获取专家知识,形成一个 具有继承性的概念层次结构.该环境为知识获取提供视图(viewing)、图示(graphing)、概 念聚类和概念发现等工具.     

Using directed hypergraphs to verify rule-based expert systems

Rule-based representation techniques have become popular for storage and manipulation of domain knowledge in expert systems. It is important that systems using such a representation are verified for accuracy before implementation. In recent years, graphical techniques have been found to provide a good framework for the detection of errors that may appear in a rule base. The authors present a graphical representation scheme that: 1) captures complex dependencies across clauses in a rule base in a compact yet intuitively clear manner and 2) is easily automated to detect structural errors in a rigorous fashion. Their technique uses a directed hypergraph to accurately detect the different types of structural errors that appear in a rule base. The technique allows rules to be represented in a manner that clearly identifies complex dependencies across compound clauses. Subsequently, the verification procedure can detect errors in an accurate fashion by using simple operations on the adjacency matrix of the directed hypergraph. The technique is shown to have a computational complexity that is comparable to that of other graphical techniques. The graphical representation coupled with the associated matrix operations illustrate how directed hypergraphs are a very appropriate representation technique for the verification task     

基于产生式系统的知识建模

如何建立系统完整的专家知识模型,并将其快速映射为面向计算机推理的人工智能语言是当前专家系统研究的重点和难点,而知识表示是其中的关键.本文将模型驱动的思想引入到专家系统领域,定义了一种基于不确定性产生式知识表示的元模型,设计并实现了相应的图形化建模工具和转换引擎,并基于此提出一种新的产生式系统应用框架.该框架在某健康信息评估专家系统中获得了成功的应用.     

军事领域本体构建研究

在仿真系统的研究过程中,面临军事领域知识获取及不同应用之间的互操作等问题.这些难题的背后实际是技术人员和军事人员之间理解一致性和机器之间理解一致性的问题.本体是关于事物本质的模型,已被广泛应用于知识分类、表达、共享及重用等方面.首先,讨论了本体的概念并给出了领域本体构建的一套通用原则和方法.然后详细分析了军事领域本体所包含的各方面知识元素,包括作战单元类、单元之间的关系、交战行为、交战规则、武器装备、作战计划等,并分别给出了它们的简单表示结构.最后概要讨论了多个本体合并时的本体检验问题.标准化的军事知识表示结构可以为技术人员和军事人员之间的交流提供统一的认识和参照,为机器之间的互操作提供一种"语言",从而消除理解上的冲突和混乱,为作战仿真系统的开发扫清了道路.     

Knowledge Acquisition Advisor (KA): An interactive knowledge-acquisition tool for expert system development

A new interactive knowledge acquisition tool, called Knowledge Acquisition Advisor (KA²), is presented in this paper. The new tool will help knowledge engineers to conduct effective knowledge-elicitation interviews with domain experts through structured knowledge acquisition for both analytic and synthetic problems. A graphic modeling data structure, called Knowledge Graph is proposed, which allows knowledge engineers to model domain problems with their images and understanding. By using Knowledge Graph, knowledge engineers are able to decompose a domain problem into several components, to model the feature of each component, and to explore their relations by linking them with sets of questions. These questions can later be employed to guide the KA interview. Moreover, KA² is particularly useful for interview through computer networks, so the knowledge acquisition can take place remotely.     

Automated knowledge acquisition for the surface-mount PWB assembly domain

The development of knowledge-based (or expert) systems for the surface-mount printed wiring board (PWB) assembly domain requires the understanding and regulation of several complex tasks. While the knowledge base in an expert system serves as a storehouse of knowledge primitives, its design and development is a bottleneck in the expert system development life-cycle. Therefore the development of an automated knowledge acquisition (KA) facility (or KA tool) would facilitate the implementation of expert systems for any domain. This paper describes an automated KA tool that helps to elicit and store information in domain-specific knowledge bases for surface-mount PWB assembly. A salient feature of this research is the acquisition of uncertain information.     

A mutation analysis tool for Java programs

Program mutation is a fault-based technique for measuring the effectiveness of test cases that, although powerful, is computationally expensive. The principal expense of mutation is that many faulty versions of the program under test, called mutants, must be created and repeatedly executed. This paper describes a tool, called JavaMut, that implements 26 traditional and object-oriented mutation operators for supporting mutation analysis of Java programs. The current version of that tool is based on syntactic analysis and reflection for implementing mutation operators. JavaMut is interactive; it provides a graphical user interface to make mutation analysis faster and less painful. Thanks to such automated tools, mutation analysis should be achieved within reasonable costs.     

An expert-system shell using structured knowledge: anobject-oriented approach

An architecture for an expert-system shell that mixes declarative nd procedural knowledge, overcoming a major problem of conventional shells, is presented. The prototype shell uses structured knowledge representations and its built-in database interface not only allows automatic extraction of data from a database management system but also provides a fuzzy database query facility. The shell's object-oriented approach to knowledge representation supports data and knowledge acquisition and management. Another feature is encapsulation which prevents object manipulation except by defined operations. A comparison of representation methods and two case studies showing System X-I's power and flexibility are included     

A knowledge extraction and representation system for narrative analysis in the construction industry

Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students’ learning.     

An interactive tool for knowledge base refinement

We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.     

A knowledge capture approach for directly acquiring team mental models

In order to better study team behavior and performance, much attention has focused on obtaining team members' mental models for the purposes of analysis and prediction. This paper describes a knowledge capture approach and a supporting graphical tool that together allow for direct acquisition of team mental models in the form of conceptual graphs. We performed acquisition experiments on over one hundred teams where team members used the tool to specify their team process. In addition to acquiring team members' models, non-team observers created “expert” models of the team process for comparison. We obtained good results on accepted measures of recall and precision compared to the “expert” models.     

面向对象表示法在火控系统故障诊断知识表示中的应用

通过与目前几种常用的知识表示方法的优缺点的对比，提出了火控系统故障诊断知识的一种新的表示方法——面向对象表示法。阐述了面向对象知识推理的特点和具体的应用。     

DSM as a knowledge capture tool in CODE environment

A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system/ process. This paper shows how DSM, a system engineering tool, is applied as a knowledge capture (acquisition) tool in a generic NPD process. The acquired knowledge (identified in the DSM) is provided in the form of Questionnaires, which are organized into five performance indicators of the organization namely ‘Marketing’, ‘Technical’, ‘Financial’, ‘Resource Management’, and ‘Project Management’. Industrial application is carried out for knowledge validation. It is found form the application that the acquired knowledge helps NPD teams, managers and stakeholders to benchmark their NPD endeavor and select areas to focus their improvement efforts (up to 80% valid).     

A Delphi-based approach to developing expert systems with the cooperation of multiple experts

Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that “time” is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the “time” factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.     

IAKA—智能化农业知识获取平台的设计与实现

IAKA（the Intelligent Agricultural Knowledge Acquisition Platform）是一种农业领域的智能化知识获取平台,为了实现农业知识获取及可视化,阐述了IAKA的思想和基于面向对象技术的系统设计和实现方案,并采用基于“以用户为中心的设计”方法设计,利用拟人知识化、可视化的技术实现了IAKA平台。     

Inference in the SOOKAT object-oriented knowledge acquisition tool

The SOOKAT (structured object-oriented knowledge acquisition tool) knowledge acquisition (KA) tool, supporting the SeSKA (seamless structured knowledge acquisition) methodology, integrates phases of KA through seamless transformations between object-oriented (OO) models.The integration of constructing a knowledge base (KB) can be extended beyond the KA process by performing inferences in instantiations of models constructed during the KA process.The models, constructed during the KA process, form a framework for performing inferences in instantiations of the models.Inferences performed in instantiations of OO models are guided by control objects (CO). Messages are sent between COs and components of the inference structure. A specific CO, possibly using subordinate COs, can be specified for each inference strategy.There exists a mutual CO for forward and backward chaining that can also be used when reasoning according to protocols. In addition, COs for problem-solving methods (PSMs), such as cover-and-differentiate or propose-and-revise, can be used.Mechanisms for importing PSMs over the Internet, as well as for generating specific COs for PSMs, are under development.     

数据库型知识的产生式表示

在专家系统构造中,需要大量的简单信息类知识作为推理的基础。传统的产生式表示,对于这类知识,具有很大的局限性。不仅无法给出知识之间的关系,而且知识库规模庞大。为了解决这个问题,文章引入了数据库型产生式(DatabaseStyleRule,DSR)的概念。某农业节水专家系统的实践应用表明,数据库型产生式(DSR)表示有着很好的效果,可显著降低知识库规模,提高知识之间的关联性。从而提高推理机的效率,减少知识获取的开销,增强知识库的可维护性和可扩展性。