Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

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.

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 (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.

No description available.

No description available.

No description available.

No description available.

This thesis discusses aspects of design, implementation and theory of expert systems, which have been constructed in a novel way using techniques derived from several existing areas of Artificial Intelligence research. In particular, it examines the philosophical and technical aspects of combining techniques derived from the traditional rule-based methods for knowledge representation, with others taken from connectionist (more commonly described as Artificial Neural Network) approaches, into one homogenous architecture. Several issues of viability have been addressed, in particular why an increase in system complexity should be warranted. The kind of gain that can be achieved by such hybrid systems in terms of their applicability to general problem solving and ability to continue working in the presence of noise, are discussed. The first aim of this work has been to assess the potential benefits of building systems from modular components, each of which is constructed using different internal architectures. The objective has been to progress the state of knowledge of the operational capabilities of a specific system. A hybrid architecture containing multiple neural nets and a rule-based system has been designed, implemented and analysed. In the course of, and as an aid to the development of the system, an extensive simulation work-bench has been constructed. The overall system, despite its increased internal complexity provides many benefits including ease of construction and improved noise tolerance, combined with explanation facilities. In terms of undesirable features inherited from the parent techniques the losses are low. The project has proved successful in its stated aims and has succeeded in contributing a working hybrid system model and experimental results derived from the comparison of this new approach with the two, primary, existing techniques.

The continuous increase in the cost of building real life projects, the high cost of researches concerning a project and the urgent demands for the project to be delivered in a short period of time have urged researchers to find a new scheme of programming in which they would be able to simulate or emulate the real life activities and projects using a computer and a procedural language. But such a scheme of programming was lengthy, tedious and costly. Therefore, it was necessary to find a specialized simulation software that would save the programmer's time and effort on the one hand and would save the client's money on the other. A number of simulation software were developed recently which contributed greatly to the solutions of the simulation problems. However, none of these languages possessed the completeness nor the independence of the use of other tools or procedural languages to compensate for their deficiencies.In this paper I have proposed a number of new ideas aimed to improve the simulation languages in general, and have implemented a number of these ideas in a software package. Chapter one describes the simulation model and the basic concepts of simulation. Chapter two describes briefly the advantages and disadvantages of using simulation software. Also it outlines the limitations of currently used simulation packages. Finally, it suggests new ideas and expectations of a complete simulation package. Chapter three describes the simulation package prototype S. E. S. and gives some implementations. Chapter four explains the basic differences between S. E. S. and SLAM II and shows areas for further research. / Department of Computer Science