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Management Information Systems•Intelligent techniques:Used to capture individualand collective knowledge and to extend knowledgebase–To capture tacit knowledge:Expert systems, case-basedreasoning, fuzzy logic–Knowledge discovery: Neural networks and data mining–Generating solutions to complex problems:Geneticalgorithms–Automating tasks:Intelligent agents•Artificial intelligence [AI] technology:–Computer-based systems that emulate human behaviorIntelligent TechniquesCHAPTER 11: MANAGING KNOWLEDGE29
Management Information Systems•Expert systems:–Capture tacit knowledge in very specific and limiteddomain of human expertise–Capture knowledge of skilled employees as set ofrules in software system that can be used by othersin organization–Typically perform limited tasks that may take a fewminutes or hours, e.g.:•Diagnosing malfunctioning machine•Determining whether to grant credit for loan–Used for discrete, highly structured decision-makingIntelligent TechniquesCHAPTER 11: MANAGING KNOWLEDGE30
Management Information SystemsIntelligent TechniquesRULES IN ANEXPERT SYSTEMAn expert system containsa number of rules to befollowed. The rules areinterconnected; thenumber of outcomes isknown in advance and islimited; there are multiplepaths to the sameoutcome; and the systemcan consider multiple rulesat a single time. The rulesillustrated are for simplecredit-granting expertsystems.FIGURE 11-6CHAPTER 11: MANAGING KNOWLEDGE31
Management Information Systems•How expert systems work–Knowledge base: Set of hundreds or thousands ofrules–Inference engine: Strategy used to search knowledgebase•Forward chaining:Inference engine begins withinformation entered by user and searches knowledgebase to arrive at conclusion•Backward chaining:Begins with hypothesis and asksuser questions until hypothesis is confirmed ordisprovedIntelligent TechniquesCHAPTER 11: MANAGING KNOWLEDGE32
Management Information SystemsIntelligent TechniquesINFERENCE ENGINES IN EXPERT SYSTEMSAn inference engine works by searching through the rules and “firing” those rules that are triggered by factsgathered and entered by the user. Basically, a collection of rules is similar to a series of nested IF statementsin a traditional software program; however, the magnitude of the statements and degree of nesting aremuch greater in an expert system.FIGURE 11-7CHAPTER 11: MANAGING KNOWLEDGE33
Management Information Systems•Successful expert systems–Con-Way Transportation built expert system to automateand optimize planning of overnight shipment routes fornationwide freight-trucking business•Most expert systems deal with problems ofclassification–Have relatively few alternative outcomes–Possible outcomes are known in advance•Many expert systems require large, lengthy, andexpensive development and maintenance efforts–Hiring or training more experts may be less expensiveIntelligent TechniquesCHAPTER 11: MANAGING KNOWLEDGE34
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