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.

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.

Knowledge Acquisition for Large-Scale Expert Systems in Transportation

1998 ◽  
Vol 1651 (1) ◽  
pp. 59-65
Author(s):  
Jeffrey L. Adler ◽  
Eknauth Persaud
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Traditional Knowledge ◽  
Large Scale ◽  
Knowledge Engineering ◽  
Problem Domain ◽  
Client Server ◽  
Server Application

One of the greatest challenges in building an expert system is obtaining, representing, and programming the knowledge base. As the size and scope of the problem domain increases, knowledge acquisition and knowledge engineering become more challenging. Methods for knowledge acquisition and engineering for large-scale projects are investigated in this paper. The objective is to provide new insights as to how knowledge engineers play a role in defining the scope and purpose of expert systems and how traditional knowledge acquisition and engineering methods might be recast in cases where the expert system is a component within a larger scale client-server application targeting multiple users.

A Desktop Expert System as a Human Factors Work Aid

1987 ◽  
Vol 31 (10) ◽  
pp. 1087-1090 ◽  
Author(s):  
Craig S. Hartley ◽  
John R. Rice
Keyword(s):  
Expert System ◽  
Human Factors ◽  
Expert Systems ◽  
Knowledge Base ◽  
System Development ◽  
Scientific Publication ◽  
Problem Area ◽  
Video Display ◽  
Problem Domain ◽  
Topic Area

The advent of increasingly powerful microcomputers, coupled with the development of small, feature-packed expert systems now makes it cost effective to provide workers with relatively inexpensive desktop expert systems. In order to evaluate the value of such systems as work aids for human factors engineers, we developed a small demonstration system using a commercially available expert system development tool, NEXPERTTM, released in 1985 by Neuron Data, Inc. of Palo Alto, CA. We selected a candidate problem area based on four criteria: 1) the problem domain had to be small enough to be covered comprehensively by a relatively small knowledge base; 2) the problem domain had to be potentially useful to video display terminal (VDT) screen designers; 3) appropriate information had to be readily available in human factors guidelines, published reports, and journal articles; and 4) the problem should provide the opportunity to exercise as many of the features of NEXPERT as possible. The topic area we selected was “video display screen color”. Our goal was to produce a job performance aid (JPA) that non-human factors VDT screen designers could use to select appropriate colors for screen features. Because the system users typically have little or no formal training in human factors, the JPA has to supply color recommendations in the form of clearly stated requirements, but with the decision rationale and additional references also immediately available for users wanting more information. Using the expert system shell provided by NEXPERT, we constructed a knowledge base containing more than one hundred IF …, THEN … rules representing knowledge gained from a detailed literature review. We initially validated our expert system by posing a wide variety of hypothetical design problems and assessing its conclusions against our expectations. Based on our work so far, we have concluded that small expert systems can be useful in providing human factors expertise to system designers. We believe that increasing use of expert systems may soon lead to changes in the typical current scientific publication format to include knowledge base rules provided by the author(s).

scholarly journals IMPLEMENTASI SISTEM PAKAR DIAGNOSA KERUSAKAN PADA CANON NP6650XX MENGGUNAKAN METODA DEMPSTER SHAFER

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Khaerul Manaf
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
System Development ◽  
Computer Software ◽  
Software Tool ◽  
Theory Of Knowledge ◽  
Monitor Screen ◽  
System Knowledge

An expert system is a computer software that has a knowledge base. Where knowledge is taken from several experts with experience working for years on a particular field of expertise. Expert systems easier to develop and specifications are not too difficult, so it can be used by computers that exist todayThe purpose of this study to design a software tool in diagnosing damage to the machine canon NP 6650XX which creates the appearance of an error code on the monitor screen machine using Dempster Shafer Method. To achieve this, research is conducted by collecting the theories associated with this machine, based on the theory of knowledge, undertake steps that expert system development, identification, conceptualization, formalization, implementation and testingThe result is a software that can provide information about damage to the machine canon NP 6650XX which such damage can lead to the appearance of an error code on the monitor screen machine.  Keywords: Expert System, Knowledge, Canon Machinery, Error, Dempster Shafer.

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.

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.

Formulation of Expert System Knowledge

1989 ◽  
Vol 33 (5) ◽  
pp. 350-350
Author(s):  
Deborah A. Mitta
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Human Factors ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Mental Models ◽  
Formal Education ◽  
Domain Expert ◽  
Cognitive Problem ◽  
System Knowledge

Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.

scholarly journals Location-based expert system for diabetes diagnosis and medication recommendation

2020 ◽  
Vol 48 (1) ◽  
Author(s):  
Mohammed A. Almulla ◽  
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Brand Names ◽  
Diabetic Patients ◽  
Diabetes Diagnosis ◽  
Effective Factors ◽  
The Past ◽  
Medical Tests ◽  
The Right

Using expert systems in the medical field has been practiced continuously for the past decades. There are attempts of using expert systems for a diabetes diagnosis. In this paper, we go further by proposing an expert system that not only diagnoses diabetes but also recommends the right medication depending on the location where the patient lives and on the symptoms of the patient and other effective factors. This system can be very helpful to many diabetic patients, especially to those who are not aware of their disease type or how to control it. The system outputs a list of names of locally available brand names of medications that suit the diabetes type of the patient and that do not pose any danger to the health of the patients according to their symptoms, effective factors, and results of the patients’ medical tests. Our expert system is capable of reasoning using either forward chaining or backward chaining. The rules in the knowledge base are collected from several medical textbooks and articles published in scientific journals, periodicals, and international conferences. To verify the content of the knowledge base, a medical expert and a pharmacist working in Kuwait were consulted.

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.

Dynamics in Knowledge

2012 ◽  
pp. 1595-1612 ◽  
Author(s):  
Shigeki Sugiyama
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Problem Solving ◽  
Small Area ◽  
Social Study ◽  
Engineering Science ◽  
The Past ◽  
Intelligent Software ◽  
System Method ◽  
Modern Engineering

Since the idea of “artificial intelligence with knowledge” had been introduced, so many thoughts, theories, and ideas in various fields of engineering, science, geology, social study, economics, and management methods have been proposed. Those things have been started as an extension of modern engineering control theories and practices. Firstly, expert system by using IF-Then rules came up to at a production spot in manufacturing, and then agent system method by using intelligent software programs for design, planning, scheduling, production, and management in manufacturing. And then after, the idea of “Knowledge” burst into the artificial intelligence field as a real aid for getting any purpose to be accomplished by having augmented the past key knowledge in terms of management (controlling). However, those augmented knowledge methods used to have usages only in a limited small area. In addition to this, lots of works have to be done before making the systems work for a target problem solving. And what is worse, lots of parts of systems have to be customized for a new application. This chapter introduces a new direction and a method in “Knowledge” by inaugurating the brand new idea of “Dynamics in Knowledge,” which will behave more flexibly and intelligently in real usages.

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