Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective

Expert systems are an evolving technology with the potential to make human expertise widely and cheaply available. The literature describing the development of expert systems generally assumes that experts willingly give up their knowledge. This is unrealistic and may be a reason why most expert system projects fail. This paper explores the problem of unwilling experts from the perspective of a knowledge engineer building an expert system. The link between knowledge and organizational power is established and human motivation theories are discussed. Finally, a new motivational approach is introduced to help the knowledge engineer deal with unwilling experts.     

An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the 'knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the 'paradox of expertise', the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.     

The design process of expert systems development: some concerns

Abstract: A discussion is presented of why some expert systems that organizations have developed are not successful. The concept of design process plays a very significant role at the outset of the expert system development process. This concept has not been the subject of much debate and attention in expert systems development. From the author's point of view, one of the main issues is how the designer (knowledge engineer) thinks about the design process. In general, the designer's process is influenced by the knowledge engineer's conception. This paper endeavors to disclose some of the main factors related to the knowledge engineer's conception of the design process and an attempt is made to put forward a conceptual model of the expert system design process. This conceptual model is an initial step towards a successful implementation of expert system projects.     

GDKAT: A goal-driven knowledge acquisition tool for knowledge base development

While knowledge-based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in-depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain-dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal-driven knowledge acquisition tool (GDKAT) that helps elicit and store experts' declarative and procedural knowledge in knowledge bases for a user-defined domain. The designed tool is implemented using the object-oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.     

基于Rough集理论的分布式知识获取模型

分布式知识获取是当前数据挖掘研究领域的热点问题之一.为了利用Rough集理论荻取分布决策表中的知识,提出了一个基于Rough集理论的分布式知识获取模型,并讨论了数据在粗糙分布式环境下,运用信息抽取算子及知识生成算子获取全局决策规则的方法.这些理论与方法扩展了Rough集理论处理多数据源的知识获取问题.实例证明了这些方法的可行性.     

mwKAT: A meta-tool-generated knowledge acquisition tool for multimedia workstations development

mwKAT is an interactive knowledge acquisition tool for acquiring domain knowledge about multimedia components. It constructs knowledge bases for a consulting system that produces the design specification for a multimedia workstation according to the user requirements.mwKAT is generated from and executed inGAS, a primitives-based generic knowledge acquisition meta-tool. It contains three acquisition primitives, namely, parameter proposing, constraint proposing, and fix proposing to construct an intermediate knowledge base represented by a dependency model. These primitives identify necessary domain knowledge and guide users to propose significant components, constraints, and fix methods into the dependency model.mwKAT also invokes knowledge verification and validation primitives to verify the completeness, consistency, compilability, and correctness of the intermediate knowledge base.     

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.     

Channeling knowledge: Expert Systems as communications media

Expert Systems (ES) are as yet imperfectly defined. Their two consistently cited characteristics are domain knowledge and expert-level performance. We propose that current structural definitions are inadequate and suggest a view of ES as communication channels. We proceed to explore the factors influencing applicability of ES technology to an enterprise and the impacts that could be expected. A consequence of this view is the idea of incremental information loss on the path from the expert to the ES user. Strategies for minimizing this loss derive naturally from our perspective and are evident in successful ES.     

Concept classifier for knowledge acquisition in frame-based systems

Frame-based systems that employ inheritance networks as a form of knowledge representation have a number of inherent knowledge acquisition problems, one of the most significant being the transfer to the representation system of knowledge itself. The problem of concept classification, and specifically that of determining the location of a new concept in an existing network inheritance hierarchy, is discussed here using an experimental knowledge-base editor, KRE. Tools which support the process of knowledge base construction must allow the user to concentrate on the domain problems, and not on low level, representation system decisions. KRE, written in C, is a knowledge acquisition tool which assists the knowledge engineer by using an interactive acquisition strategy during the process of concept classification. The processes of classification, and its advantages over other knowledge representation systems, are presented.     

信息系统开发中的知识转移研究综述

知识管理已经成为信息系统领域的研究热点,其中一个重点就是知识转移。论文对信息系统开发过程中知识转移研究文献从因素研究、开发方法、外包、动态知识流、知识分离与集成等方面进行了综述,分析了该领域的研究现状、存在的问题。未来应从知识管理的视角研究ISD开发过程,把需求分析等活动上升到知识分析的高度,使知识管理研究贯穿信息系统的整个生命周期中,及时全面地发现用户深层次的需求,为开发过程改进提供理论基础。     

基于决策熵的增量式知识获取算法

为了在知识约简中能够客观地反映决策规则的决策能力,提高约简的效率和识别率,针对动态知识获取这一问题,提出了一种基于决策熵的增量式知识获取算法。该方法利用决策熵能够客观地衡量决策表的决策能力的特点,在现有规则集基础上实现知识的动态更新,避免了重复计算从而提高了知识获取的识别率和效率。最后通过实验说明了该方法的有效性。     

Trace-driven knowledge acquisition (TDKA) for rule-based real time scheduling systems

Expert scheduling systems, which develop the schedule automatically on a real time basis, are able to respond to the changes of product demand in Flexible Manufacturing Systems (FMS). While developing an expert scheduling system, the most time-consuming and difficult step is knowledge acquisition, the process that elicits the knowledge from experts and transfers it into the knowledge base. A trace-driven knowledge acquisition (TDKA) method is proposed to extract the expertise from the schedules produced by expert schedulers. Three phases are involved in the TDKA process: data collection, data analysis, and rule evaluation. In data collection, the expert schedulers are identified and decisions made during the scheduling process are recorded as a trace. In data analysis, a set of scheduling rules is developed based on the trace. The rules are then evaluated in the last phase. If the resulting rules do not perform as well as the expert schedulers, the process returns to phase two and refines the rules. The whole process stops whenever the resulting rules perform at least as well as the expert schedulers. A circuit board production line is used to demonstrate the feasibility of the TDKA methodology. The scheduling rules perform much better than the expert schedulers from whom the rules are extracted.     

Migrant knowledge workers: An empirical study of global Sydney as a knowledge city

Global Sydney is essentially a knowledge city. The global Sydney thesis has focused on Sydney’s performances in the knowledge-based economy, especially the advanced producer services, in an increasingly integrated world economy. Sydney’s emergence as a global city has been inseparable from its migrants from overseas and elsewhere in Australia. This study aims to bridge the theses of global Sydney and migration. The focus is on migrant knowledge workers employed in the knowledge-intensive industries and highly skilled occupations, which are the most reflective of Sydney’s knowledge capacity. Using data from the 2011 Australian Census and a community survey in the Sydney region, this study reveals new insights into the different patterns between international and internal migrant knowledge workers in terms of social–economic backgrounds, drivers for moving to Sydney, and employment before and in Sydney. The findings are useful to better understand migrant knowledge workers in global Sydney, and to contribute to the global city discourse and knowledge city research.     

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.     

Knowledge tracing: Modeling the acquisition of procedural knowledge

This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has mastered each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.     

A strategy of knowledge elicitation for developing an integrated bidding/production management expert system for the precast industry

Bidding and production decisions, including the estimation of optimal mark-up on price, represent major decision problems for companies formulating a successful business strategy. The objective of this research was to develop a strategy for developing an integrated bidding/production management expert system to assess the suitability of incoming enquiries for a particular company and suggests a ‘bid/no bid’ decision. If a decision to bid is taken then the expert system should provide advice on the optimal mark-up to maximise the chance of winning potential contracts. The system developed in this paper is composed of an information system, that integrates design, estimation and production planning, and a knowledge-base to provide abstracted information and advice to managers in charge of bidding. The information system analyses the records of previous contracts and presents managers with vital information that minimises the risk of poor decisions associated with bidding. The knowledge-base is composed of intelligent rules which were elicited from previous contract records and experienced managers in charge of bidding. They are designed to advise managers on two major issues: bid/no bid and estimation of optimal tender price. A number of factors that affect bidding strategies were identified from reviewing previous bidding methodologies and surveying eight major companies in the UK by means of semi-structured interviews.