BecerraIM_ch08 - Instructor's Manual Preserving and...

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Instructor's Manual Preserving and Applying Human Expertise: Knowledge-Based Systems 8-1 45 Chapter 8 Preserving and Applying Human Expertise: Knowledge-Based Systems Teaching Objectives Introduce the student to the internal operation of knowledge-based systems, including: Rules Frames Automated reasoning Introduce the student to knowledge engineering - how to develop knowledge-based systems, the tools and the techniques Key Terms The following alphabetical list identifies the key terms discussed in this chapter. The page number for each key term is provided. Backward reasoning, p. 135 Bidirectional reasoning, p. 135 Daemons, p. 142 Developer's interface, p. 135 Development environment, p. 132 Facets, p. 141 Forward reasoning, p. 135 Frame, p. 133 Inference chain, p. 138 Inference engine, p. 132 Intelligent program, p. 130 Knowledge acquisition tool, p. 135 Knowledge base, p. 132 Knowledge-based systems, p. 129 Knowledge engineering, p. 130 Pattern matching, p. 138 Problem-specific database, p. 130 Rule interpretation, p. 149 Slots, p. 141 Structured knowledge, p. 136 Test case database, p. 135 Tracing a goal, p. 151 User interface, p. 130 Teaching Suggestions Undergraduates need to understand the basis of knowledge-based systems – the inference engine (forward and backward chaining), the knowledge base (rules, frames) and the fact base. They should be able to build a simple KBS using CLIPS.
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Instructor's Manual Preserving and Applying Human Expertise: Knowledge-Based Systems 8-2 45 Graduate students should be able to do the above and understand the role of the knowledge in the KBS. This is important in how this knowledge can be captured for future distribution in a KM process. Review Questions 1. State in your own words the difference between backward and forward chaining. What are the advantages and disadvantages of one versus the other? The most important difference is that forward chaining begins with the inputs and processes them with the available knowledge in order to identify some conclusions. Backward chaining, on the other hand, makes an assumption about a particular conclusion and attempts to use the knowledge to confirm the assumption. Ultimately, of course, backward chainers have to access the inputs, but that process is postponed until it becomes necessary to do so. The cost of obtaining inputs is one important issue when considering which form of inference to use. Backward chainers offer advantages for problems where the cost of acquiring individual inputs is high. Such may be the case if questions asked of the user are not readily known, and a need for him/her to seek the answer elsewhere is created. Backward chaining minimizes the need to get the answers by not seeking all inputs, regardless of whether they are necessary or not (as does forward chaining). If the cost of accessing inputs, however, is minimal, such as with an automatic data acquisition system, then a forward chainer may be preferable. Alternatively, the topography of the knowledge may also dictate preferences.
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BecerraIM_ch08 - Instructor's Manual Preserving and...

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