University of Technology, Sydney. Faculty of Business. / Knowledge Based Systems (KBSs) have the potential to automate a significant number of the decision making processes across organisations of all types. This represents a unique capability, not available to conventional information systems. It gives KBSs the potential to increase internal efficiency, and to enhance an organisation's competitive position. Despite these potential improvements, the impact of this capability upon an organisation introduces a host of new and complex management issues. Strategic planning for the use of KBSs in organisations is recognised as an important, but neglected area of KBS management research. In practice, KBS development methodologies are used to guide KBS strategic planning. Historically, KBS strategic planning efforts have been poor and are linked to the very high incidence of KBS failure in organisations. While KBS development methodologies may be able to identify potential KBS projects, they are unable to identify which projects have the highest organisational value. The core of the strategic planning problem is that KBS development methodologies adopt current valuation models which do not adequately assess whether investment in a KBS is worthwhile. These valuation models are designed for use in the domain of conventional information systems, but are problematic when applied to KBSs. The unique capability of KBSs to make decisions generates numerous tangible and intangible costs and benefits which cannot be captured by these current valuation models. In addition, these current valuation models fail in three key areas that are critical for adequately assessing KBSs value. First, they do not provide disaggregated information on costs and benefits, many of which are peculiar to KBSs. Second they do not classify these costs and benefits into categories that are meaningful to managers making KBS investment decisions. Third, despite the fact that current valuation models cannot measure intangible costs and benefits, they do not utilise the perceptions of KBS employees to measure them. Using employee perceptions to measure intangible costs and benefits is valid if a recognised psychological model is used to measure perceptions of value. A valuation model specifically designed for KBSs, which addresses these key areas, is needed by managers planning for an organisation's KBS strategy to enable them to identify KBS investments with the highest organisational value. The aim of this thesis is to propose and verify such a model. To achieve this, the case study research methodology was used. The chosen case is a large sales and manufacturing organisation. At the time of study this organisation was developing three KBSs and was interested in being able to measure the relative value of the systems. The study found that the proposed KBS valuation model presented in this thesis overcame the inadequacies of current valuation techniques. First, the results indicate that value of a KBS to an organisation can be assessed by measuring KBS value perceptions of three key employee groups involved in the KBS lifecycle. These groups were found to be: KBS project managers; knowledge domain experts; and KBS users. Employee perceptions of KBS value were measured by adapting the Theory of Reasoned Action (TRA) which reliably produced valid measures of perceived KBS value. Second, the results indicate that the KBS value perceptions were able to be expressed as disaggregated tangible and intangible costs and benefits. Third, these disaggregated costs and benefits were able to be classified into three categories of value found to be common to all KBSs and meaningful to management. These categories are: time; finances; and quality. Finally, a new graphical technique, termed a "KBS value graph", designed to visually portray to managerial decision makers, the perceived value of a KBS was developed. It lucidly portrays perceived KBS value while supporting the three critical areas of KBS valuation.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (Doctoral)--Universität Osnabrücks, 2006. / Includes abstract and bibliographical references (p. 216-231).

Thesis (M.S.)--University of Pennsylvania, 1981. / "August 1981." Includes bibliographical references.

Thesis (M.S.)--University of Florida, 2004. / Title from title page of source document. Document formatted into pages; contains 77 pages. Includes vita. Includes bibliographical references.

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The ability to remember past experiences enables a system to improve its performance as well as its competence. For example, a system might be able solve problems faster by adapting previous solutions. Additional tasks, such as avoiding unwanted behavior by detecting potential problems, monitoring long-term goals by remembering what subgoals have been achieved, and reflection on past actions, become feasible. As the tasks that an intelligent system accomplishes become more and more complex, so does the experience it acquires in the process. Such experience has a temporal extent and is expressed in terms of concepts and relations with deep semantics associated to them. Memory systems should be able to deal with the temporal aspect of experience, exploit this semantic knowledge for storage and retrieval and do so in a scalable fashion. However, relying just on experience will not achieve a broad coverage, as it needs to be used in conjunction with other reasoning mechanisms. That is why we need the ability to add episodic memory functionality to intelligent systems. Today's knowledge-based systems are complex software applications and the ability to develop them in a modular fashion, using generic, reusable components is essential. We propose to separate the episodic memory from the system that uses it and to build a generic, reusable memory module that can be attached to a variety of applications in order to provide this functionality. Its goal is to provide accurate, scalable, efficient and content-addressable access to prior episodes. Having such a reusable memory module should allow research to focus on the generic aspects of memory representation, organization and retrieval and its interaction with the external application and it should also reduce the complexity of the overall system. In this dissertation we propose a set of general requirements that any memory module should provide regarding memory encoding, storage and retrieval. We present an implementation that satisfies these requirements and evaluate it on three different tasks: plan synthesis, plan recognition and Physics problem solving. The memory module proved easily adaptable to these tasks, providing fast, accurate and scalable retrieval.

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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.

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