Case research on knowledge acquisition: observations and lessons

1991 ◽  
Vol 6 (2) ◽  
pp. 97-120 ◽  
Author(s):  
Christine Chan ◽  
Izak Benbasat
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
System Development ◽  
Relevant Literature ◽  
System Construction ◽  
Organizational Setting ◽  
Knowledge Engineer ◽  
Case Research ◽  
Acquisition Processes

AbstractExpert systems are being built despite the widely acknowledged problem of acquiring knowledge from experts. This study attempts to understand how knowledge acquisition is conducted in practice by investigating three expert system development projects. A CASE research methodology is adopted, and data is collected through unobtrusive observation, from taped protocols of knowledge acquisition sessions, retrospective interviews with the participants involved, and deliverables produced. The variables examined include the problem domain, the domain expert, the knowledge engineer, the knowledge acquisition process, the expert system construction process, potential users, organizational setting, and the expert system itself. The knowledge acquisition processes for three expert systems in the domains of law of negligence, telephone line fault diagnosis, and wastewater treatment have been examined. By juxtaposing the observations drawn with findings from the relevant literature, the study makes prescriptive suggestions on considerations and techniques for future acquisition efforts, and provides data for hypothesis generation in further research.

Knowledge-acquisition tools with explicit problem-solving models

1993 ◽  
Vol 8 (1) ◽  
pp. 5-25 ◽  
Author(s):  
William Birmingham ◽  
Georg Klinker
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Code Generation ◽  
System Development ◽  
Generalization Error ◽  
Domain Experts ◽  
The Past

AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

A Delphi Algorithm that Integrates Knowledge for Expert System Development

1989 ◽  
Vol 33 (17) ◽  
pp. 1159-1159
Author(s):  
Terre L. Layton ◽  
Newton C. Ellis ◽  
R. Dale Huchingson
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Delphi Technique ◽  
Development Process ◽  
System Development ◽  
Policy Decision ◽  
Model Verification ◽  
Automated Technique

A rapid growth of expert system development in various fields of study will likely occur in this decade. Two prerequisites are needed in order for this to happen: strong social need and technical feasibility. Given that both factors presently exist, a few areas where expert systems can help significantly include: (1) providing an interactively accessible source of updated and well-organized knowledge, and (2) assisting a user in decision making. The current research reviews areas of Artificial Intelligence that relate to the process of knowledge acquisition for expert systems. Until very recently, the primary technique for knowledge acquisition has been the time-consuming process of interviews. Typical techniques include: structured and unstructured interviews, questionnaires, and verbal reporting which incorporates protocol analysis. The functions involved in one or more of the techniques encompass extraction of meaning, data inference, and rule induction coupled with retrospective comment analysis, and behavioral observations. The purpose of the current research is to explore different avenues for data acquisition when dealing with multiple knowledge sources with the objective to develop an automated technique for knowledge acquisition. The Delphi Technique is the primary technique investigated in this study, and the result is the Delphi Manager algorithm which is based on the original version of the Delphi Exercise modified to benefit the expert system development process. Other users of the algorithm include: (1) model verification and validation, (2) forecasting, and (3) opinion polls for policy decision making. Although there are additional uses, the Delphi Manager is primarily formulated for the expert system development process. The Delphi Manager was validated by using an existing knowledge base (KB) that was compiled by a paper and pencil version of the Delphi Technique. This existing KB was part of a dissertation by Randall F. Scott entitled “A Computer Programmer Productivity Prediction Model.” The Delphi Manager has the potential to reduce significantly the time needed to collect and analyze new data. In addition, its user-friendly interface reduces the need for an advanced computer user either to build a questionnaire or to install a help facility. The program provides context sensitive help which is input by the developer through a series of templates. The Delphi Manager is also flexible enough to accommodate anyone from a novice to an advanced programmer. Improvements are suggested that are designed to provide additional program functionality and applications.

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

Differential Learning Expert System in Data Management

2009 ◽  
pp. 597-604
Author(s):  
R. Manjunath
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Management ◽  
Knowledge Acquisition ◽  
User Interfaces ◽  
High Performance ◽  
Evolutionary Model ◽  
Human Expert ◽  
Incremental Development ◽  
Differential Learning

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.

Using the Internet for Knowledge Acquisition in Expert Systems Development: A Case Study

1996 ◽  
Vol 11 (3) ◽  
pp. 223-234
Author(s):  
Kathleen K. Molnar ◽  
Ramesh Sharda
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
Electronic Mail ◽  
System Development ◽  
Cost Savings ◽  
Lessons Learned ◽  
The Internet ◽  
Domain Experts ◽  
Use Of The Internet ◽  
Multiple Experts

Knowledge acquisition is a major task in expert system development. This paper proposes one way of acquiring knowledge for expert system development: through the use of the Internet. Internet resources (e.g. Usenet groups, ListServ discussion lists, archive sites and on-line literature/database searches) are knowledge sources. Internet tools such as newsreaders, electronic mail, Telnet, FTP, gophers, archie, WAIS and World Wide Web provide access to these sources. The results of an exploratory study that examined the use of the Internet as a knowledge source are presented here in conjunction with a framework for using the Internet in the planning phase. Four major advantages can be found in this: the availability of multiple experts in multiple domains, the interaction of domain experts and end users, time/cost savings, and convenience. The lessons learned and some additional issues are also presented.

Analysis of Expert System in the Field of Machine Design

2012 ◽  
Vol 479-481 ◽  
pp. 565-568
Author(s):  
Hong Qi Luo ◽  
Meng Yu Wang
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Inference Engine ◽  
Knowledge Bases ◽  
Machine Design ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Design Expert ◽  
Cad System

Intelligent CAD system can be formed if integrating the expert system and mechanical CAD. Components of expert system were analyzed, including integrated databases, knowledge bases, knowledge acquisition, inference engine, explanation mechanism and human-computer interface. The model of design-evaluate-redesign was introduced and discussed. Current situation of research on design expert systems was summarized.

A PROLOG-BASED CHINESE EXPERT SYSTEM

1988 ◽  
Vol 02 (01) ◽  
pp. 17-24
Author(s):  
HSU LOKE SOO
Keyword(s):  
Expert System ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Domain Expert ◽  
Knowledge Engineer ◽  
Knowledge Application ◽  
Design And Implementation ◽  
Expert System Shell ◽  
Natural Language Interface ◽  
Application Module

This paper presents the design and implementation of a Chinese Expert System Shell which is based on a Chinese Prolog interpreter. The system is divided into three parts: the knowledge acquisition module, the knowledge application module and the inference engine. The knowledge engineer defines the syntax of the language to be used by himself and by the users when they interact with the system. The natural language interface is table driven and can be modified easily. The system also caters for the case when the domain expert finds it difficult to articulate the rules, but is able to give examples. An inductive engine is included to extract rules from examples.

scholarly journals Issues in the design and implementation of expert systems

1987 ◽  
Vol 1 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Clive L. Dym
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Future Development ◽  
Stages Of Development ◽  
Task Selection ◽  
Design And Implementation ◽  
Institutional Issues ◽  
The Future ◽  
Run Time

This article discusses the issues that arise in the design and implementation of expert systems. These issues include: task selection; the stages of development of expert system projects; knowledge acquisition; languages and tools; development and run-time environments; and organizational and institutional issues. The article closes with some speculation about the future development of expert systems.

Expert Systems and Special Education

1986 ◽  
Vol 53 (3) ◽  
pp. 235-239 ◽  
Author(s):  
Alan M. Hofmeister ◽  
Joseph M. Ferrara
Keyword(s):  
Special Education ◽  
Expert System ◽  
Expert Systems ◽  
System Development ◽  
Computer Programs ◽  
Development Tools ◽  
Potential Applications

Expert systems are computer programs designed to replicate human expertise in a variety of areas. This article discusses the characteristics of these programs as well as recently available expert system development tools. The article also suggests potential applications for expert systems within the field of special education. Finally, the article reviews recent efforts to apply expert systems technology to special education problems.

scholarly journals Penalaran Berbasis Aturan Untuk Diagnosa Awal Penyakit Anjing Menggunakan Teorema Bayes

2018 ◽  
Vol 2 (2) ◽  
pp. 530-535 ◽  
Author(s):  
Sella Marselena ◽  
Ause Labellapansa ◽  
Abdul Syukur
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Rural Areas ◽  
Medical Personnel ◽  
Bayes Theorem ◽  
Search Method ◽  
Early Symptoms ◽  
Forward Chaining

Many pets can be played with, socialize and even live together with humans. Numbers of animal clinics have increased to provide care for pets. This study focuses on Dog as pet. Desease and improper treatment of dog will adversely affect the Dog. In dealing with the problem of Dog disease, Dog owners may experience difficulties due to limited number of clinics and veterinarians, especially in rural areas. As a solution, Artificial Intelligence is used by using expert systems that can help inexperienced medical personnel diagnose early symptoms of Dog disease. The search method used in this research is Forward Chaining and Bayes Theorem method to handle uncertainties that arised. Based on knowledge acquisition, 3 diseases were obtained with 38 simptoms and 60 cases. Based on the tests conducted then obtained the sensitivity value of 80%, the value of accuracy of 88.6% indicates that this expert system is able to diagnose dog diseasesKeywords: Dog, Expert System, Forward Chaining, Bayes Theorem.  

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