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BecerraIM_ch07 - Instructor's Manual Technologies to Manage...

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Instructor's Manual Technologies to Manage Knowledge: Artificial Intelligence 7-1 45 Chapter 7 Technologies to Manage Knowledge: Artificial Intelligence Teaching Objectives Introduce the student to knowledge as being an important facet of intelligent behavior Introduce the student to expertise in the context of knowledge Introduce artificial intelligence as a facilitating technology for knowledge management Introduce the student to knowledge-based systems Key Terms The following alphabetical list identifies the key terms discussed in this chapter. The page number for each key term is provided. Artificial intelligence, p. 101 Algorithms, p. 109 Associational knowledge, p. 119 AUTHORIZER'S ASSISTANT, p. 117 COOKER, p. 117 Domain knowledge, p. 102 Fuzzy sets, p. 124 GenAID, p. 117 Goal state, p. 105 GUIDON, p. 117 Heuristics, p. 109 Heuristic functions, p. 109 Heuristic search, p. 109 Inference mechanism, p. 116 Initial state, p. 105 Knowledge engineering process, p. 119 Model-based reasoning systems, p. 120 Motor skills expertise, p. 120 MYCIN, p. 116 PROSPECTOR, p. 116 Random search, p. 110 Set membership function, p. 125 Shells, p. 116 Solution space, p. 105 Symbol manipulation, p. 102 Systematic blind search, p. 111 Tactical knowledge, p. 118 XCON, p. 116
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Instructor's Manual Technologies to Manage Knowledge: Artificial Intelligence 7-2 45 Teaching Suggestions Undergraduates need to understand the general concept of representing human intelligence in computers, and how this revolves very much around knowledge. The undergraduate student should also understand the difference between the early state space search methods that were relatively knowledge poor, and the more modern knowledge-intensive knowledge-based systems. The latter are much more useful in KM processes. The graduate student should understand at a deeper level the role of knowledge in artificial intelligence and in knowledge-based systems. They should comprehend the difference in the nature of the knowledge found in solution spaces vs. that found in knowledge bases for KBS. They should also be aware of the difference between complex and simple knowledge in the context of KBSs. Furthermore, the various types of expertise should also be understood. Review Questions 1) What are the two types of knowledge typically found in intelligent systems? Describe each type and distinguish between them. This question can be answered in two different interpretations. The first is the level of the knowledge. The answer to this first interpretation is general knowledge and technically- specific knowledge (also called complex knowledge). General knowledge is such as can be found integrated into the solution space in the early AI state space search techniques. This knowledge can represent, for example, the rules of a game such as chess. Complex (a.k.a.
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BecerraIM_ch07 - Instructor's Manual Technologies to Manage...

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