Formulation of Expert System Knowledge

An expert database system is an expert system which is developed on databases using DBMS technology to manage facts and rules. Frame-based expert systems are widely used as the knowledge representation for such expert systems with large knowledge bases. Many systems have the ability to connect to external databases. Facts stored in databases can be loaded into expert system's knowledge base and inference is performed by the inference engine of the expert system. In many cases, such external facts are required several times for each inference. Thus, a lot of communication traffic takes place. This research work present the design and implementation of a frame-based object-relational database system which has a tight coupling between the expert system and the external knowledge base. The external knowledge base also use frame as its knowledge representation. More over, it has its own inference engine so that inference can be perform on the knowledge base side and the results, not only simple facts, are sent back to the expert system for further inference. A medical consultation system is used as an illustrated example an Oracle lOg object relational DBMS is used as the database platform.

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.

A New Approach In Learning For Intelligent Multi Agent Systems

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 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

The study aimed to design and develop an intelligent knowledge-based system prototype which mimics the human domain experts’ knowledge that can be used by environmental impact assessment review committee (EIARC) members in the assessment and preparation of environmental impact assessment (EIA) reports for mountain resort projects. Specifically, the system aimed to strengthen and improve the implementation of EIA among mountain resort development projects through an objective, transparent and fast-paced evaluation of EIA documents. The knowledge-based system was developed using e2gRuleWriter to generate its knowledge base and e2gRuleEngine as expert system shell. The human domain experts were purposively selected as the source of knowledge of the system. Through knowledge engineering, the knowledge of the domain experts was captured and became the basis for the development of the expert system rules which were presented in production rules format to assess the project description and activities of mountain resort developments. The developed expert system rules were validated using resolution principle to ensure its correctness. The human domain experts further validated the developed expert system rules by confirming that the knowledge-based system produces correct recommendation. Keywords: Artificial Intelligence, Knowledge Engineering, Environmental Impact Assessment, Expert System

Causal probabilistic models have been suggested for representing diagnostic knowledge in expert systems. This paper describes the theoretical basis for and the implementation of an expert system based on causal probabilistic networks. The system includes model search for building the knowledge base, a shell for making the knowledge base available for users in consultation sessions, and a user interface. The system contains facilities for storing knowledge and propagating new knowledge, and mechanisms for building the knowledge base by semi-automated analysis of a large sparse contingency table. The contingency table contains data acquired for patients in the same diagnostic category as the intended application area of the expert system. The knowledge base of the expert system is created by combining expert knowledge and a statistical model search in a model conversion scheme based on a theory developed by Lauritzen & Spiegelhalter and using exact tests as suggested by Kreiner. The system is implemented on a PC and has been used to simulate the diagnostic value of additional clinical information for coronary artery disease patients under consideration for being referred to coronary arteriography.

This eight-volume report presents guidelines for performing verification and validation (V&V) on Artificial Intelligence (Al) systems with nuclear applications. The guidelines have much broader application than just expert systems; they are also applicable to object-oriented programming systems, rule-based systems, frame-based systems, model-based systems, neural nets, genetic algorithms, and conventional software systems. This is because many of the components of AI systems are implemented in conventional procedural programming languages, so there is no real distinction. The report examines the state of the art in verifying and validating expert systems. V&V methods traditionally applied to conventional software systems are evaluated for their applicability to expert systems. One hundred fifty-three conventional techniques are identified and evaluated. These methods are found to be useful for at least some of the components of expert systems, frame-based systems, and object-oriented systems. A taxonomy of 52 defect types and their delectability by the 153 methods is presented. With specific regard to expert systems, conventional V&V methods were found to apply well to all the components of the expert system with the exception of the knowledge base. The knowledge base requires extension of the existing methods. Several innovative static verification and validation methods for expert systems have been identified and are described here, including a method for checking the knowledge base {open_quotes}semantics{close_quotes} and a method for generating validation scenarios. Evaluation of some of these methods was performed both analytically and experimentally. A V&V methodology for expert systems is presented based on three factors: (1) a system`s judged need for V&V (based in turn on its complexity and degree of required integrity); (2) the life-cycle phase; and (3) the system component being tested.

This paper investigates a fault diagnosis expert system base on artificial neural network (ANN-FD-ES) for mixed-signal circuits. We built this system by given a faulty circuit. Based on the transient response testing (TRT) on the mixed-signal circuits, both analog and digital signal characteristics are unified. The system includes knowledge base, reasoning machine, explanatory, and interface. The diagnostic portion of the system is based on neural-calculation approach, which is used to establish the knowledge base composed of the weigh values and threshold values. With this approach, conclusions are developed directly from the outputs of the net, rather than the domain knowledge in traditional expert system. Compared with the traditional expert system, this system is good at processing data. The experimental results show that the system not only improves the shortcoming of the traditional expert system such as the deficiency on knowledge acquisition and self-learning capability, but also achieves a satisfied fault diagnosis efficiency.

The use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the "gold standard" of day 21 progesterone. The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.

Industrialization is a fundamental objective for most developing countries in their efforts to achieve economic growth. However, simple quantitative increases in industrial output or in total national income cannot accurately measure other changes in the development process such as attainment of social objectives, technological advances, efficiency considerations, etc. Assessing such measures can help planners in developing countries to better formulate industrial policies, and stimulate improvements in performance. It can also help aid donors and international lenders. This study explores and applies a method of classification, ranking, and comparison of countries according to their level of industrial development. It is based on a comprehensive analysis of the different factors affecting, directly or indirectly, industrial growth. These factors include criteria--both quantitative and qualitative--related to effectiveness, comparative performance, efficiency, impacts, sustainability, and capabilities. Aggregation of these elements results in a composite index of industrial progress. Based on this analysis, a generic prototype performance evaluation system was developed. An expert systems approach was attempted in building the prototype in view of the heuristic nature of the problem. The system's knowledge base was acquired from domain experts, as well as from the literature. The system was tested using real data from a sample of 20 countries. The prototype runs on personal computers (IBM and compatibles) using an expert systems shell (VP-Expert) interfacing with database and spreadsheet files.

In this paper we analyse the explanation facilities of several expert systems using a framework based on factors relevant to the structure and content of a generated explanation. We compare this with the lack of such facilities in expert system shells, and lay out criteria for an explanation facility within an expert system shell for education. We then describe their realisation in the explores (explanation oriented expert system) shell, where domain-independent templates allow questions to be asked about the domain of any knowledge base being consulted. In response, the shell generates domain-based explanations, with further information available through follow-up questions related to the original question, the generated explanation, and the available knowledge. This facility allows a user to explore and learn about a knowledge domain, and has implications for the use of expert system shells in education.

An expert system for selecting the methods for retrofitting fatigue cracking in steel bridges is developed. The expert system involves a knowledge base with new knowledge representations and a new inference engine. The knowledge is based on 90 cases of fatigue cracking in existing steel bridges, and a knowledge base is constructed with knowledge being represented as production relations. All the relations in the knowledge base are assumed to have a certainty factor. The new inference engine was developed in the C language so that it has the ability to learn by using the knowledge‐based network model. The inference engine is able to modify relations with a certainty factor. Therefore, the knowledge‐based network model can grow into an optimum knowledge‐based network. The present system is evaluated for the actual fatigue cracking in three steel bridges. It is found that the system is able to infer reasonable methods for retrofitting cracked members.

Expert systems have now proven their value in a variety of enterprises ranging from medicine to geology. Small expert systems are at work in numerous commercial and industrial sites, but large systems are still rare. Besides the difficulties inherent in the collection of useful and correct domain knowledge, large knowledge bases present special problems in data management. These problems often cause a good pilot system to founder when expanded to an operational size.This paper addresses the difficulties of efficiently handling large knowledge bases. The methods discussed result from viewing a knowledge base as a database and analyzing the knowledge as one would analyze a database application, using Chen's Entity-Relationship (E-R) Diagrams [CHEN76] to build a relational database representation. Besides providing a different perspective on the management of knowledge bases, such a method facilitates the use of a commercial DBMS for this task.Following these ideas the simple assertions of the knowledge base are viewed as one entity type and the rules themselves as another, with IF and THEN being separate relationships between these two types. This decomposition is reasonable for capturing the knowledge represented by the rules and leads to a relational description which is considerably different than usual rule representations. More precisely, the E-R diagram of Figure 1 is taken as the basis of the model, where the rectangles represent entities and the ellipses represent relationships between the entities connected to that ellipse. The unboxed words such as rule# are attributes of the associated entities and correspond to the information to be saved in the database.The E-R diagram in Figure 1 can be converted to the relational database tables shown in Figure 2 where rule# is a name or a number that identifies the rule, if_part is a simple assertion Ai (antecedent), then_part is a simple assertion Ci (conclusion), and conf is a number representing the confidence in that deduction.This results in a very different data organization from the usual collection of knowledge found in rule-based systems. Beyond that analysis, very specific implementation techniques are suggested for knowledge bases, using both standard relational data bases and simpler file-handling techniques such as those available in standard programming languages. Caution must be taken, however, before simply trying to turn knowledge base management over to a commercial DBMS, since the behavior of “queries” within a rule base is very different from that of ordinary database queries. Comparisons are made to such recent works as those of Deering, Debanne, and Bouzegoub which suggest a variety of implementations for knowledge bases.Often in the modeling of a domain, one discovers a hierarchical relation among the basic concepts, vocabulary, or objects of study. Such an IsA or AKindOf hierarchy can be used to create a segmentation of the knowledge base resulting in significant efficiency improvements within the system. This possibility is explored in some detail.Throughout this work, emphasis is given to dynamic rule bases which are subject to frequent changes and which, therefore, require a generalized inference engine and flexible data structures. Often due to execution speed requirements some domain knowledge is encoded as procedures within the inference engine. Although this may improve the efficiency of the system, it undermines the generality and flexibility that are also required. This paper investigates a promising method for incorporating such procedural inclusions within the general knowledge base structure. The approach developed allows the absorption of changes without restructuring of the knowledge base or modifications to the inference engine, and maintains system transparency, simplicity, and expandability.

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