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3. KNOWLEDGE ACQUISITION TECHNIQUESThe approach used for knowledge acquisition determines both the quality of knowledge and the amount of effort required for its acquisition, so the technique selected greatly affects the performance of the expert system and the resources required for its development. The growing recognition of the importance of knowledge acquisition has resulted in the development of various techniques, methodologies, and tools for automated knowledge acquisition. This section reviews techniques used in psychology and social sciences for revealing expert knowledge structures and processes. Techniques that involve the concept of machine learning (e.g., induction), neural networks, simulation, web-based knowledge acquisition, and automated knowledge acquisition tools designed for specific applications are not discussed in this chapter (see Cox, Al-Ghanim, and Culler, 1995; Osyk and Vijayaraman, 1995 for neural-based knowledge acquisition). Many knowledge acquisition techniques and tools have been developed. Each technique has its strengths and limitations. How well a knowledge engineer can utilize them will depend on his/her selection of appropriate techniques and tools, which in turn determine the quality of knowledge acquired, the amount of effort needed, and the skills required. Interviewing is the most commonly used method in requirements elicitation for analysis and design of information systems. It is also widely used in eliciting knowledge from domain experts for expert systems development (Olson and Rueter, 1987). In general, there are two forms of interviewing. The basic form involves question-answer sessions between the knowledge engineer and the expert. These free-form or unstructured interviews are usually started by the engineer's asking "How do you solve this problem?" Follow-up questions usually reflect further explanation or clarification of some points that the expert has made. The process is fairly unstructured. A distinct advantage of free-form interviews is that knowledge engineers can elicit unanticipated information. However, there are difficulties with this technique. First, as people become more experienced at performing certain tasks, they become less aware of the cognitive processes involved in their performance. They cannot explicitly describe their reasoning process step by step. Second, there are certain biases and fallibilities in human reasoning. When reasoning about the entire sequence, people tend to anchor on items that occur early in a sequence. People see what they expect to see. When asked to describe their reasoning process and problem-solving methods, experts tend to provide reconstructed versions of their reasoning and omit some components that may be important to solve the problem because they assume them to be obvious and explicit. Moreover, experts may get tired and become bored with repeating what to them seems to be obvious information. People do not talk in complete sentences. Speech is marked by phrases, asides, "ers," etc. Neither the expert nor the knowledge engineer knows at the time which parts of the dialogues are important. Consequently, all details of the interviews must be recorded, transcribed, and analyzed. This makes knowledge acquisition a tedious and time-consuming process. Structured interviewing, a more effective form of the interviewing technique, is goal-oriented. It forces organization of the communications that take place between a knowledge engineer and experts. The structure provided by clearly stated goals reduces the interpretation problems inherent in free-form (e.g., unstructured) interviews and allows the knowledge engineer to prevent the distortion caused by domain expert subjectivity. This technique is more effective because it forces the domain expert to be systematic in attending to interview tasks. Empirical evidence has shown that the structured interviewing technique improves the efficiency and effectiveness of knowledge acquisition, and can be applied to knowledge acquisition from multiple experts. When this technique is used, experts either fill out a set of carefully designed questionnaire cards or answer questions raised by the knowledge engineer, making use of an established domain model of the business decision-making activity to capture the subjective and qualitative aspects of decision making. Questionnaires can be particularly useful in discovering the objects of the domain, in uncovering relationships, and in determining uncertainties. Observations, an obvious way of discovering how an expert solves a real problem, involves observing how he or she does it. This technique allows an expert to work in the accustomed environment without interruptions by the knowledge engineer and gives the knowledge engineer insights into the complexities of a problem. One important decision that must be made before employing this technique is how the expert's performance is to be recorded. One easy way is for the knowledge engineer to observe and take notes. The other alternative is to videotape the problem-solving process. A major limitation of this technique is that the underlying reasoning in an expert's mind is usually not revealed in his or her actions. Protocol analysis, usually referred to as "thinking aloud," is a form of data analysis that has its origin in clinical psychology. When employing this technique, a knowledge engineer describes a problem scenario and asks an expert to talk about his or her thinking process while solving the problem. Experts find it much easier to talk about specific examples of problems than to talk in abstract terms. The "thinking aloud" process is videotaped and analysis of it is based on transcripts. Once the transcripts are produced, protocols must be analyzed based on a systematic breakdown of the information to produce a structured model of the expert's knowledge. The goal is to identify the kinds of objects that the expert sees, the attributes of those objects, the relationships among those objects, and the kind of inferences drawn from these relationships. The advantage of this technique is that the transcripts describe specific actions and rationales as the expert thinks through and talks about the decision-making process. There is no delay between the act of thinking of something and reporting it. Protocol analysis is not appropriate for all kinds of tasks. Some of the tasks that are suitable include various puzzles, elementary logic problems, chess strategy, binary-choice sequence prediction, concept identification for the induction of various logical and sequential concepts, various understanding tasks, and those tasks for which verbalization is a natural part of thinking. However, the validity and applicability of pure "thinking aloud" protocols in verbally complex situations has been questioned by many researchers. Repertory grid analysis, which had its origin in Kelly's personal construct theory (1955), aims at gaining insights into the expert's mental model of the problem domain. It involves an initial interview with an expert, a rating session, and analyses that cluster both the objects and the traits on which the items were rated. In the initial interview, the expert is asked to identify some objects in the domain of expertise. After a set of objects has been identified, the expert is asked to compare three of these objects at a time, in each case naming a trait that two of the objects possess but the other does not. The expert is then asked to identify an opposite of that trait. The expert further provides a scale to rate the importance of the traits. The same process is repeated until all the objects have been compared and traits to differentiate them identified. In the rating session, these objects are rated according to the traits identified and scales assigned. At any stage the expert can add more objects or traits or alter entries in the grid. In this way the process heightens his or her awareness of how he or she views the problem. Once the rating grid has been established, a computer program can be used to cluster the objects and cluster the traits. This technique is useful in extracting subjective data, but there are a number of difficulties associated with its use. First, unless the number of objects in the problem domain is small, an enormous number of comparisons need to be made. Second, it is not always easy to identify traits that differentiate objects. It sometimes takes a long time to make just one comparison and, as a result, the process becomes very time-consuming.
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