The scope and limitations of first generation expert systems

Expert systems are a well-known and well-received technology. It was thought that the performance of a domain expert could not be duplicated by a machine. Expert systems technologies have shown this to be a false belief, and indeed have demonstrated how experts themselves can come to depend on expert systems. Expert systems enjoy widespread use in industrial domains and further uses are planned. The growth in acceptance has been explosive since about 1986. Continued rampant growth appears to depend on cracking the so-called knowledge acquisition bottleneck.The knowledge acquisition bottleneck limits the scalability of expert systems. While it is relatively straightforward to populate a small-scale knowledge base, it becomes more difficult to maintain consistency and validity as the knowledge base grows. Thus, it is important to automate the knowledge acquisition process. A by-product of this process is that any failure of the expert system will be “soft.”The question is, “What impact can machine learning technologies have on knowledge acquisition in the large?” The true test will be on prospective industrial applications in areas such as biology, education, geology, medicine, and scientific discovery. Machine learning technologies include expert systems, genetic algorithms, neural networks, random seeded crystal learning, or any effective combinations.Relevant subtopics include: Second generation expert systems — progress and prognosisRepertory GridsThe importance of symbolic and qualitative reasoningThe acquisition of fuzzy rulesThe best learning paradigm or combination of paradigmsImpact of machine learning on explanation systemsThe role of toy domains such as chessAutomatic programming revisitedApplications to computer vision, decision support systems, diagnosis, helpdesks, optimization, planning, scheduling, et al.Implementation issues using SIMD and MIMD platformsSources for joint sponsorshipForming industrial partnershipsForming alliances abroad

A framework for the integration of expert systems with multimedia technologies

SASHA is a model-driven application shell which generates expert systems for fault isolation and repair in complex electronic systems. SASHA incorporates an expert system shell and a knowledge acquisition tool into a unique mechanism that elicits data directly from the domain expert and outputs a rule-based diagnostic expert system.SASHA employs causal-model and rule-based representations. It uses a causal model to identify the structure, behavior and relation of a domain object. It uses rules to direct the diagnostic problem solver. SASHA's initial knowledge base consists of a domain independent generalized model of the system under analysis. This underlying model drives knowledge acquisition and the consequent generation of an expert system. SASHA introduces a new concept of Pattern Rules, which are a generalized skeleton of the diagnostic problem solver. During diagnosis, when applied to the domain specific Structural Knowledge base, Pattern Rules dynamically instantiate the actual domain rules. Based on the Pattern Rules notion, three types of rule base organization are proposed and analyzed: Fully Patternized Organization (FPO), Partly Patternized Organization (PPO) and Customized Domain-specific Organization (CDO).

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

The author discusses the increasing use of expert system technology in the customer support area. She provides an introduction to expert systems, and considers examples of customer support applications. Many companies have already begun experimenting with expert systems in their customer support effort. The two companies discussed, are Xerox and Symbolics, utilize expert systems technology for product types which range from reasonably simple to fairly complex. >

Expert systems applications in quality management: An integrative approach

Second generation expert systems

An optimized design of a matrix treatment involves fluid selection and acidizing simulations to predict the outcome of the treatment; Many matrix acidizing treatment designs and fluid selections have been successfully accomplished by utilizing expert system technology. However, none of these present a complete and optimized result (i.e., by utilizing the output of the expert system to predict the acidizing outcome using an acidizing numerical simulator). In the meantime, several acidizing computer simulation studies have been conducted separately. This paper presents a study which integrates the treatment design, particularly the fluid selection process, and acidizing simulation for sandstone formations. Required parameters for sandstone acidizing such as acid type, concentration, volume, and injection rate/pressure are first selected using an expert system. The output from the expert system is further used for the input to an acidizing numerical simulator (UTACID). A new sandstone acidizing reaction model, appropriate for a high-temperature environment, and anisotropic medium have been implemented into UTACID to enhance the performance of the simulator. The expert system and the simulator have been integrated to provide an optimization tool for sandstone acidizing treatment design and simulation.

DECADE - A hybrid expert system for catalyst selection

Readers have problems navigating through large hypermedia systems. Expert system technology is a promising means to enhance such hypermedia systems. Firstly, a hypertext model is introduced. Then, hypermedia systems and expert systems are compared and the ways in which expert technology might be applied are considered. Finally, the application of expert system technology (using organization and domain knowledge, indexing, and agents) to the nodes, links and interface of the hypermedia system is presented.

The medical and technology fields are rapidly advancing, leading many people to use computers to help diagnose, prevent, and treat human diseases. One major issue in the medical world is the imbalance between the number of patients and doctors. Additionally, most people lack medical training, so when experiencing symptoms of a disease, it is often difficult to immediately know the correct steps to take. Eye diseases vary in severity, ranging from mild to severe. One common eye disorder affecting many people is refractive error, which generally falls into two categories: hyperopia (farsightedness) and myopia (nearsightedness). Early detection of symptoms related to refractive errors requires accurate and prompt diagnosis. Therefore, with the rapid development of technology, it is essential to develop systems capable of early detection of eye diseases, especially refractive errors, by using technology that mimics human expert capabilities, such as expert systems. This expert system integrates expert knowledge within two main environments: the development environment and the consultation environment, helping the community diagnose diseases more easily and efficiently. For example, the use of the Certainty Factor method in expert systems enables the calculation of diagnostic certainty levels based on the combination of symptoms reported by patients and expert knowledge, achieving a confidence level of up to 96.7%. This demonstrates that expert system technology can be a valuable tool in addressing the imbalance between patients and doctors while improving access to faster and more accurate diagnoses. To build such systems, Microsoft Visual Studio .NET provides a complete set of tools for developing ASP.NET web applications, XML Web Services, desktop applications, and mobile applications. Within Visual Studio, .NET programming languages such as Visual Basic, Visual C++, Visual C# (CSharp), and Visual J# (JSharp) are used in a unified integrated development environment (IDE), enabling developers to efficiently share tools and resources to create reliable and user-friendly expert system applications. The system was developed and tested using symptom data from 40 patients collected at the Tanjung Sarang Elang Community Health Center. The testing showed a diagnostic accuracy of up to 96.7% in detecting symptoms of both hyperopia and myopia.

In January 1987, the University of Karlsruhe and Siemens started a joint research project on expert system application for supporting digital switch maintenance. Major results of the first project phase are summarized. Maintenance phases supported by an expert system are defined. Expert system technology is used to help technicians to handle alarms of the Siemens digital switching system EWSD correctly and quickly. The implemented and successfully tested expert system prototype deals with coordination processor alarms in EWSD exchanges. >

Expert systems: A short introduction

Expert systems use the expertise of experienced people to evaluate and solve problems in a decision making domain. Such systems use their knowledge base to model a human approach to the problems' solution. Expert system technology has been applied to many areas of problem solving. This paper describes an expert system approach to completing partially routed printed circuits boards. An experimental expert system has been implemented to solve the associated local blockage problem. This research should provide the foundation for the development of an expert system for a Computer Aided Design Workstation.

Expert systems (ES) technology has many existing and potential uses as input into the agricultural production process. ES are simply computer programs that attempt to emulate human expertise for problem solving purposes. This technology is well suited to agricultural problem solving because of its ability to encode existing knowledge within specific application areas and then apply this knowledge within the problem solving process. ES effectively leverage management's input into agricultural production systems by allowing for the assimilation of all available knowledge pertinent to the task at hand. There are many types of agricultural ES and these can generally be categorized into: Crop Management Advisors, Livestock Management Advisors, Planning Systems, Pest Management Sytems, Diagnostic Systems, Conser-vation/Engineering Systems, Process Control Systems and Marketing Advisory Systems. The cost of ES development can be high. Potential projects, therefore, should be subjected to a benefit/cost analysis to ensure that development efforts are well targeted. ES development and agricultural research are complementary processes and should become more coordinated within an integrated knowledge production and implementation cycle. Key words: Expert systems, knowledge, management, agriculture, research