Fundamentals of expert systems

Expert systems (ES) were among the earliest branches of artificial intelligence (AI) to be commercialized. But how successful have they actually been? Many well-publicized applications have proven to be pure hype, numerous AI vendors have failed or been completely reorganized, major companies have reduced or eliminated their commitment to expert systems, and even Wall Street has become disillusioned -- a predicted $4 billion market proving to be smaller by an order of magnitude. Yet, in spite of these setbacks, there are many companies who remain enthusiastic proponents of the technology, and continue to develop important ES applications.The paper describes an investigation exploring how the first wave of commercial expert systems, built during the early and mid-1980s, fared over time. An important subset of these systems, identified in a catalog of commercial applications compiled in 1987, was located through a telephone survey, and detailed information on each systems was gathered. The data collected show that most of these systems fell into disuse or were abandoned during a five-year period from 1987 to 1992, while about a third continued to thrive. Quantitative and qualitative analysis of the data further suggested that the short-lived nature of many systems was not attributable to failure to meet technical performance or economic objectives. Instead, managerial issues such as lack of system acceptance by users, inability to retain developers, problems in transitioning from development to maintenance, and shifts in organizational priorities, appeared to be the most significant factors resulting in long term systems disuse.

Early expert systems: where are they now?

Expert Systems, Knowledge Development and Utilization, and Sustained Competitive Advantage: A Resource-Based Model

As expert system technology spreads, the need for verification of system knowledge assumes greater importance. This paper addresses the issues involved in demonstrating a rule-based system to be free from error. A holistic perspective is adopted, wherein sorces, manifestations, and effects of errors are identified. A general taxonomy is created, and the implications for system performance and development outlined. Existing strategies for knowledge verification are surveyed, their applicability assessed, and some directions for systematic verification suggested.

Issues in the verification of knowledge in rule-based systems

This paper reviews a wide range of knowledge acquisition techniques in the context of attempts to achieve a systematic methodology. These have been poorly documented by expert system builders, who are often inclined to overvalue textbooks and the ways experts themselves claim they solve problems. No one method has a universal advantage; each has some value. Techniques should be selected to suit the domain, the task, the expert and the knowledge engineer. Knowledge acquisition involves creating a conceptual model of expert knowledge and reasoning, from analysis of data elicited by these techniques. A survey of the literature indicates increasing emphasis on tools for knowledge acquisition, used directly by experts. Several projects currently directed towards providing a proper epistemological foundation for knowledge acquisition are discussed and compared. None has yet produced a complete epistemologically sound methodology; however, recognition of the need to create a conceptual model at the knowledge level (rather than the symbol level) is an important advance. An extensive bibliography is appended.

First generation expert systems: a review of knowledge acquisition methodologies

This memorandum reviews the factors that constitute an Expert System Building Tool (ESBT) and evaluates current tools in terms of these factors. Evaluation of these tools is based on their structure and their alternative forms of knowledge representation, inference mechanisms and developer/end-user interfaces. Next, functional capabilities, such as diagnosis and design, are related to alternative forms of mechanization. The characteristics and capabilities of existing commercial tools are then reviewed in terms of these criteria.

The Nature and Evaluation of Commercial Expert System Building Tools

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The Nature and Evaluation of Commercial Expert System Building Tools