This thesis explores whether computer-based expert systems can be used in planning and, if so, under what circumstances. Expert systems are computer-based programs that solve problems in a way that mimics the human reasoning process. Expert system reasoning relies upon logic and rules-of-thumb rather than the numerical and mathematical algorithms of most other computer programs. Planning has been interpreted in the professional context of urban and regional planning, rather than the cross-disciplinary fiscal or project management planning often implied in computer literature. To determine expert systems could be useful to planners, the reasons for incompatibility between the nature of planning and conventional computers models of the past are explored. Advantages and disadvantages of expert systems are considered. Expert systems represent substantial improvement in areas where conventional programs are inadequate. Chapters 2 to 5 form the theoretical base of the thesis. Chapter 2 explains the fundamentals of expert system reasoning and how it differs from other computer software. Chapter 3 outlines reasons why this technology might appeal to planners. Chapter 4 introduces some of the disadvantages of expert systems, including technical limitations, ethical and legal issues. Chapter 5 introduces general guidelines to help the reader understand what type of planning tasks might benefit from the use of this new tool. Chapters 6 to 8 consider issues raised in the preceding chapters. Three Canadian systems now at the forefront of expert systems applications to planning are reviewed in chapters 6, 7 and 8 Respectively. The first application, HERMES, is an emergency planning application. It advises emergency response personnel during crises involving hazardous materials. SCREENER is an environmental planning application. It assists environmental officers at Transport Canada to assess simple capital projects for environmental impact statements and screen out more complicated ones for further review. The third application used for illustrative purposes, PLANCHECKER, is a municipal planning example. The system assists plan checkers at City Hall in assessing building plans. The three case-studies satisfy the task suitability guidelines quite well and appear to be successful applications of expert systems to planning. It is still too early to draw definite conclusions, but it is likely the technology will prove useful to planners. Planners should be prepared understand both the potential and limitations of expert systems so they can use the technology wisely. / Applied Science, Faculty of / Community and Regional Planning (SCARP), School of / Graduate

Expert systems are being developed 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. The variables examined include the expert, knowledge engineer, problem domain, organizational setting, the knowledge acquisition process, the expert system construction process, and the expert system itself. A case research methodology is adopted and data is collected through observation and taped protocol of knowledge acquisition sessions, post facto interviews with the participants involved, journalistic accounts kept by the subjects, and deliverables produced. Three cases on expert systems built in the domains of law of negligence, telephone line fault diagnostic, and wastewater treatment have been investigated. By juxtaposing the observations drawn from these cases with the findings reported in the literature, this inquiry contributes to the current understanding of the knowledge acquisition process. / Business, Sauder School of / Graduate

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

Thesis (doctoral)--Technische Universiteit Delft, 1991. / Summary and vita in Dutch and English; "stellingen" in Dutch. "Stellingen" inserted. Includes bibliographical references (p. 227-252) and index.

Thesis (doctoral)--Technische Universiteit Delft, 1991. / Summary and vita in Dutch and English; "stellingen" in Dutch. "Stellingen" inserted. Includes bibliographical references (p. 227-252) and index.

Adaptive systems are systems whose characteristics evolve over time to improve their performance at a task. A fairly new area of study is that of adaptive rule-based systems. The system studied for this thesis uses meta-knowledge about rules, rulesets, rule performance, and system performance in order to improve its overall performance in a problem domain. An interesting and potentially important phenomenon which emerged is that the performance the system learns while solving a problem appears to be limited by an inherent break-even level of complexity. That is, the cost to the system of acquiring complexity does not exceed its benefit for that problem. If the problem is made more difficult, however, more complexity is required, the benefit of complexity becomes greater than its cost, and the system complexity begins increasing, ultimately to the new break-even point. There is no apparent ultimate limit to the complexity attainable.

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