Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

基于经验反馈模型的模具企业知识获取框架

针对模具企业知识集成过程中出现的经验知识提取困难等问题,提出了基于经验反馈模型的知识获取框架。研究了面向模具生命周期的知识获取方法,实现了基于本体的知识提取和表达,采用概念图对经验进行可视化建模,并使用Web本体语言（Web Ontology Language,OWL）对知识进行统一的描述与存储。阐述了该框架的结构与运行原理,并在此基础上实现了基于面向服务架构的模具企业集成平台知识获取服务。该框架满足模具企业设计制造经验性强等特点,采用符合业务流程的知识获取过程和可视化经验表达方法,降低了知识获取的难度和成本。     

Interactive 3D model acquisition and tracking of building block structures

We present a prototype system for interactive construction and modification of 3D physical models using building blocks. Our system uses a depth sensing camera and a novel algorithm for acquiring and tracking the physical models. The algorithm, Lattice-First, is based on the fact that building block structures can be arranged in a 3D point lattice where the smallest block unit is a basis in which to derive all the pieces of the model. The algorithm also makes it possible for users to interact naturally with the physical model as it is acquired, using their bare hands to add and remove pieces. We present the details of our algorithm, along with examples of the models we can acquire using the interactive system. We also show the results of an experiment where participants modify a block structure in the absence of visual feedback. Finally, we discuss two proof-of-concept applications: a collaborative guided assembly system where one user is interactively guided to build a structure based on another user's design, and a game where the player must build a structure that matches an on-screen silhouette.     

概念格在不完备形式背景中的知识获取模型

为了使概念格模型具有更强的数据处理能力,消除不完备信息带来的影响,针对经典概念格的局限性,本文将粗糙集中的粒化思维融入到概念格中。首先探讨了概念格视角下的信息粒化方法,然后提出了基于等价类和基于极大相容类的知识获取方法,最后给出了实例分析。这些方法一方面有助于概念格与粗糙集的融合,另一方面也为探索不完备形式背景的分析处理机制提供了有益思路。     

Knowledge acquisition using hypertext

This paper addresses the issue of supporting knowledge acquisition using hypertext. We propose a way of tightly integrating hypertext and structured object representation, using Artificial Intelligence (AI) frames for the basic representation of hypertext nodes. Epistemologically, a dual view of the resulting space is of interest. One view is that of hypertext which emphasizes nodes containg g text, including formal knowledge representation. The other view focuses on objects with certain relationships, which define a semantic network. Both in hypertext and in semantic networks the relations between chunks of knowledge are explicitly represented by links. However, in today's hypertext systems a node typically contains just informal text and references to other nodes. Our approach additionally facilitates the explicit representation of structure “inside” hypertext nodes using partitions. We show the usefulness of such a tight integration for knowledge acquisition, providing several features useful for supporting it based on a level of basic hypertext functionality. In particular, we sketch a method for doing knowledge acquisition in such an environment. Hypertext is used as a mediating “semiformal” representation, which allows experts to directly represent knowledge without the immediate support of knowledge engineers. These help then to make this knowledge operational, supported by the system's facility to provide templates as well as their links to the semiformal representation. As an example of our results of using this method of knowledge acquisition, we illustrate the strategic knowledge in our application domain. More generally, our approach supports important aspects of (software) engineering knowledge-based systems and their maintenance. Also their user interface can be improved this way.     

Knowledge acquisition by random sets

In this article we investigate knowledge acquisition (KA) and its relationships to random sets. Based on random set theory, we develop some estimation theorems and procedures for set-valued statistics such as nonparametric estimators. Under random interval assumption, we establish some special possibility distributions that can be easily implemented in KA tools. The knowledge studied here are rules describing relationships between various concepts, as used in diagnosis (pattern recognition) expert systems. © 1996 John Wiley & Sons, Inc.     

Knowledge acquisition and interface design

Describes tools, techniques, and concepts to optimize user interfaces. The best way to ensure that a software system is friendly and works is to base it on the intended users' mental models (how they view the world), knowledge structures (what they know and how they have organized it), and work processes. The author uses a team of engineers to systematically acquire and analyze user and domain knowledge and to translate that knowledge into user-interface design decisions. This front-end analysis method, combined with knowledge-acquisition techniques, lets one build user-centered systems     

Knowledge acquisition and the web

Knowledge-acquisition research started in the eighties as a small research community focusing on knowledge-intensive problems in relatively small domains. In this paper we look at the influence the Web has had on knowledge acquisition and vice versa. To this end we discuss in some depth four topics, namely the ontology language OWL, the vocabulary language SKOS, the notion of ontology alignment and the potential of semantic search. Even from this limited selection of research issues related to “Web knowledge” it is safe to conclude that the Web has had a large impact on knowledge acquisition, but also the other way around.     

Knowledge acquisition techniques for feature recognition in CAD models

Automatic Feature Recognition (AFR) techniques are an important tool for achieving a true integration of design and manufacturing stages during the product development. In particular, AFR systems offer capabilities for recognising high-level geometrical entities, features, in Computer-Aided Design (CAD) models. However, the recognition performances of most of the existing AFR systems are limited to the requirements of specific applications. This paper presents automatic knowledge acquisition techniques to support the development of AFR systems that could be deployed in different application domains. In particular, a method to generate automatically feature recognition rules is proposed. These rules are formed by applying an inductive learning algorithm on training data consisting of feature examples. In addition, a technique for defining automatically feature hints from such rule sets is described. The knowledge acquisition techniques presented in this study are implemented within a prototype feature recognition system and its capabilities are verified on two benchmarking parts.     

Knowledge acquisition for effective and efficient use of engineering software

The problem of effective and efficient use of engineering software can be thought of as a Pareto optimal problem. However, the complexity of modern engineering software precludes the possibility of acquiring complete knowledge of the software's Pareto optimal set. Instead, heuristic knowledge must be acquired.To this end, the paper proposes a knowledge acquisition procedure. The use of a knowledge acquisition system, which may be computerized, forms an integral part of this procedure. An illustrative example is provided.     

具有自适应能力的背景模型构建算法

对基于视频的车辆检测中的背景差分法进行了研究,提出了一种新的背景模型构建算法.该算法在初始化过程中有选择地采用动态帧数的图像作为计算帧,使背景模型的初始化具有较高的精度和效率;在背景更新时采用动态加权系数,使背景学习具有较快的速度;在进行背景差分时采用动态阈值,既使背景更新具有较高的准确度,又使视频分割具有较高的精度.经实验验证,该算法能够很好地检测出车辆,并具有较高的精度和效率.     

A generic model for building design

The design of building structures has benefited considerably through computer automation, but further developments in this field are still required. This paper presenis a generic approach to computer automation of the detailed design of building structures. Because of its high level of abstraction, the resulting model is applicable to a wide range of structure types. Other advantages include the use of a consistent data model for software design and implementation, abstract data types for the representation of engineering data, the ability to represent heuristic knowledge, and the ability to evaluate design results in an intelligent manner.     

一种履带式本体构建模型

为了有效地指导本体构建,创建了履带式本体构造模型来描述本体的构造方法。定义和描述了构成此模型的基本要素,包括本体（上层本体、领域本体和应用本体）和本体构造步骤（分析、设计、开发、部署和评估）,并在此基础上做出了定性分析,说明了顶级本体驱动开发方法、领域本体驱动开发方法和应用领域开发方法各自的特点。通过对此模型的分析和应用,以指导在本体开发中使用正确的开发方法。     

不完备形式背景中的知识获取方法

重点讨论了不完备形式背景中的相容概念以及基于相容概念的相容规则获取方法,同时给出了相应的构造算法。为了压缩规模庞大的相容规则集合,提出了一条推理规则来减少生成冗余相容规则的数目,最终得到一个完备的无冗余的相容规则集合。通过参数的设定可以满足不同用户的实际需求,实例证明该方法是有效的。该方法不仅为不完备形式背景的处理提供了一条新的途径,同时也为更深入的研究奠定了基础。     

Knowledge acquisition method from domain text based on theme logic model and artificial neural network

In order to acquire knowledge from domain text such as failure analysis text of aviation product, a framework is proposed to enhance the efficiency and accuracy of knowledge acquisition. In this framework, sentence templates are defined to extract the meta-knowledge and RDF is used to describe the extracted knowledge. After the preprocessing steps, the authors propose a new model: theme logic model (TLM) to present all the themes of a piece of text and the logical relations among different themes. In this model, the text of each theme can be represented as an attribute–value vector based on domain ontology. Meanwhile, the logical relations are the domain knowledge to be acquired. The theme logic model then will be transformed to the training set of the artificial neural network to acquire the failure analysis knowledge. After training process, acquired knowledge will be extracted by SD method from the artificial neural network and represented by rules. Therefore, a prototype is developed to acquire knowledge from failure analysis reports of aviation product. Empirical results show that the framework can acquire knowledge from domain text efficiently.     

Knowledge acquisition from batch semiconductor manufacturing data

Semiconductor manufacturing data consist of the processes and the machines involved in the production of batches of semiconductor circuit wafers. Wafer quality depends on the manufacturing line status and it is measured at the end of the line. We have developed a knowledge discovery system that is intended to help the yield analysis expert by learning the tentative causes of low quality wafers from an exhaustive amount of manufacturing data. The yield analysis expert, by using the knowledge discovered, will decide on which corrective actions to perform on the manufacturing process. This paper discusses the transformations carried out within the data from raw data to discovered knowledge, and also the two main tasks performed by the system. The features of the inductive algorithm performing those tasks are also described. Yield analysis experts at Lucent Technologies, Bell Labs Innovations in Spain are currently using this knowledge discovery application.     

Knowledge acquisition as a constructive modeling activity

Knowledge acquisition is a constructive modeling process, not simply a matter of “expertise transfer.” Consistent with this perspective, we advocate knowledge acquisition practices and tools that facilitate active collaboration between expert and knowledge engineer, that exploit a serviceable theory in their application, and that support knowledge-based system development from a life-cycle perspective. A constructivist theory of knowledge is offered as a plausible theoretical foundation for knowledge acquisition and as an effective practical approach to the dynamics of modeling. In this view, human experts construct knowledge from their own personal experiences while interacting with their social constituencies (e.g., supervisors, colleagues, clients patients) in their niche of expertise. Knowledge acquisition is presented as a cooperative enterprise in which the knowledge engineer and expert collaborate in constructing an explicit model of problem solving in a specific domain. From this perspective, the agenda for the knowledge acquisition research community includes developing tools and methods to aid experts in their efforts to express, elaborate, and improve their models of the domain. This functional view of expertise helps account for several problems that typically arise in practical knowledge acquisition projects, many of which stem directly from the inadequacies of representations used at various stages of system development. to counter these problems, we emphasize the use of mediating representations as a means of communication between expert and knowledge engineer, and intermediate representations to help bridge the gap between the mediating representations themselves, as well as between the mediating representations and a particular implementation formalism. © 1993 John Wiley & Sons, Inc.     

Knowledge acquisition for expert systems in accounting and financial problem domains

Since the mid-1980s, expert systems have been developed for a variety of problems in accounting and finance. The most commonly cited problems in developing these systems are the unavailability of the experts and knowledge engineers and difficulties with the rule extraction process. Within the field of artificial intelligence, this has been called the ‘knowledge acquisition’ (KA) problem and has been identified as a major bottleneck in the expert system development process. Recent empirical research reveals that certain KA techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. This paper presents a mapping between these empirical studies and a generic taxonomy of expert system problem domains. To accomplish this, we first examine the range of problem domains and suggest a mapping of accounting and finance tasks to a generic problem domain taxonomy. We then identify and describe the most prominent KA techniques employed in developing expert systems in accounting and finance. After examining and summarizing the existing empirical KA work, we conclude by showing how the empirical KA research in the various problem domains can be used to provide guidance to developers of expert systems in the fields of accounting and finance.