The work described in this thesis stems from the idea that expert systems should be able to accurately and appropriately handle uncertain information. The traditional approaches to dealing with uncertainty are discussed and are shown to contain many inadequacies. The Dempster-Shafer, or D-S, theory of evidence is proposed as an appealing theoretical basis for representing uncertain knowledge and for performing inexact reasoning in expert systems. The D-S theory is reviewed in some detail; including its approaches to representing concepts, to representing belief, to combining belief and to performing inference. The D-S implementation approaches pursued by other researchers are described and critiqued. Attempts made early in the thesis research which failed to achieve the important goal of consistency with the D-S theory are also reviewed. Two approaches to implementing D-S theory in a completely consistent manner are discussed in detail. It is shown that the second of these systems, a frame network approach, has led to the development of a fully functional prototype expert system shell called FRO. In this system, concepts are represented using D-S frames of discernment, belief is represented using D-S belief functions, and inference is performed using stored relationships between frames of discernment (forming the frame network) and D-S belief combination rules. System control is accomplished using a discrete rule-based control component and uncertain input and output are performed through an interactive belief interface system called IBIS. Each of these features is reviewed. Finally, a simple but detailed example of an application of a frame network expert system is provided. The FRO system user's documentation is provided in the appendix. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Since MYCIN, explanation has become a fundamental feature of knowledge-based systems (KBS). Among the common deficiencies of KBS explanations, the most acute one is the lack of knowledge. This dissertation research investigates the use of explanations provided with hypertext for increasing the availability and accessibility of domain knowledge. The ultimate objective is to determine the behavioral and cognitive basis of the use of hypertext in providing KBS explanations. Two informationally equivalent KBS were comparatively studied in a laboratory setting: one used hypertext to provide explanations, while the other one used conventional lineartext. The experiment involved 26 experienced professionals, and 29 undergraduate and graduate students specializing in accounting. Subjects used the experimental KBS to work on a realistic problem of financial analysis. Both the process and outcomes of explanation use were assessed. Outcome variables included improvement in decision accuracy, trust in the KBS, and perceived usefulness of explanations. In addition to questionnaires used to measure decision accuracy and perceptions, computer logs were used to capture the number, type, and context of explanation use. Thinkingaloud procedures were used to assess the nature of explanation use. Results indicate that the use of hypertext for providing explanations significantly improved decision accuracy, and influenced users' preference for explanation types, and the number and context of explanation requests. Enhanced accessibility to deep explanations via the use of hypertext significantly increased the number of deep explanations requested by both novices and experts. Verbal protocol analysis shows that the lack of knowledge and means of accessing deep explanations could make it difficult to understand KBS recommendations, and that deep explanations could improve the understandability of KBS advice, especially in cases where unfamiliar domain concepts were involved. In the hypertext group, about 37% of the deep explanations were requested in the context of judgment making, rather than in the abstract. While only about 28% of the deep explanations requested by the lineartext group were the How type, 42% were the How type for the hypertext group. Experts and novices had different preferences for explanation types. Experts requested a much higher percentage of How, and lower percentages of Why and Strategic explanations, than novices. Verbal protocol analysis illustrates that experts and novices used explanations for different purposes. / Business, Sauder School of / Management Information Systems, Division of / Graduate

M.Sc. (Computer science) / Knowledge representation suffers from certain problems, which is not a result of the inadequacies of knowledge representation schemes, but of the way in which they are used and implemented. In the first part of this dissertation we examine the relational model (as used in relational database management systems) and we examine frames (a knowledge representation scheme used in expert systems), as proposed by M. Minsky [MIN75]. We then provide our own definition of frames. In the second part, we examine similarities between the two models (the relational model and our frame model), establishing frames as a superclass of relations. We then define normalization for frames and examine how normalization might solve some of the problems we have identified. We then examine the integration of knowledge-based systems and database management systems and classify our normalization of frames as such an attempt. We conclude by examining the place of normalization within the expert system development life cycle

This thesis describes development of the Tarot game for mobile devices. The game is developed in Java - J2ME. The application allows to play Tarot to one through four people. The player intelligence represented by the mobile device is implemented as an expert system. The expert system rules can be changed without necessity to recompile the application. Designed and implemented expert system uses a lightweight version of Prolog language as a knowledge base. The system requires the rules of precisely defined form specified in this thesis.

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Knowledge acquisition is not only the most important but also most difficult task knowledge engineers face when they begin to develop expert systems. One of the first problems they encounter is the need to identify at least one individual with appropriate expertise who is able and willing to participate in the development project. They must also be able to use a variety of techniques to elicit the knowledge that they require. These include such traditional knowledge acquisition methods as interviewing, thinking-aloud protocol analysis, on-site observation, and repertory grid analysis. As expert system applications have become more complex, knowledge engineers have found that they must work with and tap the domain knowledge of not one but several individuals. They have also discovered that the traditional methods do not work well in eliciting the knowledge residing in a group of individuals. The complexity of the systems, the difficulties inherent in working with multiple experts, and the lack of appropriate tools have combined to make the knowledge acquisition task even more arduous and time consuming. Group Decision Support Systems (GDSS) have been proven to be useful tools for improving the efficiency and effectiveness of a multiplicity of group activities. It would appear that by bringing experts together in a GDSS environment and using computer-based tools to facilitate group interaction and information exchange, a knowledge engineer could eliminate many of these problems. This research was designed to explore the possibility of using a GDSS environment to facilitate knowledge acquisition from multiple experts. The primary research question was "Does A GDSS environment facilitate the acquisition of knowledge from multiple experts?" The principle contributions of this research are (1) demonstration of the first use of a GDSS environment to elicit knowledge from multiple experts; (2) establishment of a methodology for knowledge acquisition in a GDSS environment; (3) development of process models for acquiring knowledge; (4) development of guidelines for designing and evaluating group support tools; and (5) recognition of some implications of using a computer-supported cooperative approach to extract knowledge from a group of experts. (Abstract shortened with permission of author.)

The primary purpose of the study was to develop an expert system that could C D perform medical diagnoses In selected problem areas, and C2) provide diagnostic Insights to assist medical students In their training. An expert system Is a computer-based set of procedures and algorithms that can solve problems In a given domain. Two research questions were proposed. The first was "Given a problem space defined by a matrix of diseases and symptoms, can a computer-based model be derived that will consistently perform accurate and efficient diagnoses of cases within that problem area?" The second question was "If the techniques derived from the model are taught to a medical student, is there a subsequent improvement of diagnostic skill?" An expert system was developed which met the objectives of the study. It was able to diagnose cases in the two problem areas studied with an accuracy of 94-95%. Furthermore, it was able to perform those diagnoses in a very efficient manner, often using no more than the theoretical minimum number of steps. The expert system employed three phases: rapid search by discrimination, confirmation by pattern matching against prototypes, and elimination of some candidates (impossible states) by making use of negative information. The discrimination phase alone achieved accuracies of 73-78%. By comparison, medical students achieved mean accuracies of 54-55% in the same problem areas. This suggests that novices could improve their diagnostic accuracy by approximately 20% by following the simple rules used in the first phase of the expert system. Curricular implications are discussed. When 49 first-year medical students at the Texas College of Osteopathic Medicine were exposed to some of the insights of the expert system by means of a videotaped 10- minute lecture, their diagnostic approach was modified and the accuracy of their diagnoses did improve. However, the degree of Improvement was not statistically significant. Recommendations for further research are made.