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Chapter 2
Expert System Technology: Knowledge Acquisition

Yihwa Irene Liou


CONTENTS

1. Introduction
2. People Issues
2.1. Selecting Domain Experts
  2.2. Single vs. Multiple Experts
  2.3. Role of Knowledge Engineer, End-Users, and Managers
3. Knowledge Acquisition Techniques
4. Techniques for Collaborative Knowledge Acquisition
5. Knowledge Acquisition Methodology
6. Future Trends and Summary
References

1. INTRODUCTION

Since Artificial Intelligence (AI) was introduced in the early 1970s, the goal of AI scientists has always been to develop computer programs that can think and solve problems as intelligently as human experts. Expert systems are computer programs that use domain-specific knowledge to emulate the reasoning process of human experts. It was not until the late 1970s that AI scientists realized that the problem-solving power of a computer program mainly derives from the knowledge it possesses rather than the inference mechanism it employs.

Knowledge acquisition is the process of extracting, structuring, and organizing knowledge from several knowledge sources, usually human experts so that the problem-solving expertise can be captured and transformed into a computer-readable form. Knowledge is the most important component of expert systems. The captured knowledge forms the basis for the reasoning process of an expert system. Without explicitly represented knowledge, an expert system is no more than a computer program.

The increasing complexity of expert systems applications dictates the involvement of many experts in building those systems. Collaborative knowledge acquisition is broadly defined as the process of collaboratively extracting problem-solving expertise from a team of experts. The collective expertise enables an expert system to incorporate more comprehensive domain knowledge so that it may function more effectively than an expert system that was built from an individual expert's knowledge.

The process of assimilating the expertise of several experts into an expert system is not easy, particularly when these experts are trained in different disciplines. The differences not only appear in problem-solving strategies taken by each expert, but also appear in what heuristic is applied to solve the problem. Furthermore, the difficulty arises because of the communications barriers among experts and between experts and the knowledge engineer(s). How to facilitate the knowledge acquisition process involving multiple experts becomes a major challenge to knowledge engineers.

There are three primary concerns of the knowledge acquisition task: the involvement of appropriate human resources; the employment of proper techniques to elicit knowledge; and a structured approach to performing the knowledge acquisition task. We discuss these three areas in the following sections.

2. PEOPLE ISSUES

Identifying appropriate domain experts and involving proper people in the knowledge acquisition process is critical to the success of knowledge acquisition. Those who are involved in the knowledge acquisition process include: (1) domain experts who have had years of experience working in the application domain; (2) knowledge engineers who possess technical skills in eliciting knowledge, representing knowledge, and implementing expert systems; and (3) users and managers.

2.1. SELECTING DOMAIN EXPERTS

By analyzing the domain and the problem characteristics, it is possible to pinpoint sources of expertise. This is a joint responsibility of managers of the organization, users of the target system, and knowledge engineers. Attributes that should be considered when selecting domain experts include:

  1. Domain expertise, experience, and reputation -- The experts chosen should have expertise and experience in the specific aspect of the domain for the target expert system. Practicing experts, who are currently active in domain tasks, are the domain experts who should be identified. Leading experts are recognized by their colleagues and clients. The reputations of the experts are sometimes the major determinant of the credibility of a deployed expert system. It is also helpful to select experts with some background or interests in AI and expert systems.
  2. Personal characteristics and attitudes -- Domain experts not only should have skills in the domain but also should have skills in communicating their knowledge, judgment, and experience and the methods they use to apply these to a particular task. Other desirable attributes include a sense of humor, being a good listener, a sense of commitment, cooperativeness, patience, being easy to work with, persistence, and honesty.
  3. Availability -- The most common problems in knowledge acquisition are caused by time demands on an "already-busy" expert. Therefore, management's commitment of experts' time to a project should be secured. If problems with access are anticipated, an individual should not be selected as the primary expert regardless of his/her experience and characteristics.


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