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Decision Support And Business Intelligence Systems 9th Edition Dursun Delen Efraim Turban, Ramesh Sharda - Solutions
4. Join the artificial intelligence discussion group at groups.yahoo.com/group/pcai/join. Trace postings on the topics of this chapter over the past 2 years. Prepare a report about your findings.
3. Survey your class by having everyone write down a height representing tall, medium, and short for men and for women. Tally the results and determine what is meant by tall, medium, and short in a fuzzy way. Create the membership functions in these sets and examine the results.
2. Examine the marketplace for consumer products that incorporate fuzzy logic (sometimes this feature is called continuous logic). Try some of the products available in your area. Determine the advantages and disadvantages (if any) of these products over their nonfuzzy counterparts.
1. Identify a real-world application problem that is suitable for CBR and develop a plan for crafting an intelligent system that integrates case-based and rule-based approaches.
8. Many companies induce employees and customers to use self-service functions such as updating their own addresses or finding the balance in their banking accounts. Explain how the technologies described in this chapter can facilitate this type of self-service.
7. Solve the knapsack problem from Section 13.4 manually, and then solve it using Evolver. Try another code (find one on the Web). Finally, develop your own genetic algorithm code in Visual Basic, C++, or Java.
5. Relate the problem in Exercise 4 to a solution using a genetic algorithm.
4. You are trying to identify a specific number in the set 1–16. You can ask questions such as, “Is this number in the set 1–8?” The answer can be only yes or no. In either case, you continue to ask more questions until you identify the number.a. How many questions are needed in the worst
3. Express the following statements in terms of fuzzy sets:a. The chance for rain today is 80 percent. (Rain? No rain?)b. Mr. Smith is 60 years old. (Young?)c. The salary of the president of the United States is$250,000 per year. (Low? High? Very high?)d. The latest survey of economists indicates
2. Identify a newsgroup that is interested in SVM. Post a question regarding recent successful applications of SVM and see what feedback you get. What are the latest concerns and questions in the emerging field of SVM?
1. Access home.earthlink.net/~dwaha/research/casebased-reasoning.html. Examine the latest CBR research and demo software. How is CBR different from rulebased concepts? Try some reasoning software, compare that method to rule-based inferencing, and write about your experience in a report.
10. Why are there so many different names and definitions for intelligent agents? Does this help or hurt the development of the technology? Explain.
9. Why is the use of intelligent agents increasing exponentially?
8. For what types of problems are SVM most appropriate?Discuss your choices in the context of requirements of those problem scenarios.
7. Discuss the pros and cons of SVM as they compare to ANN.
6. Discuss the logic of combining natural language processing, voice recognition, and expert systems.
5. Discuss the advantages and disadvantages of integrating multiple methods for developing complex intelligent systems. Describe all the possible integrations among CBR, ANN, genetic algorithms, fuzzy logic, and rule-based systems and assess their feasibility.
4. Describe three advantages of fuzzy reasoning and provide an example to support each.
3. How can an investor use genetic algorithms to make a fortune in the stock market? Can a system based on a genetic algorithm perform better than one based on a neural network? Why or why not?
2. CBR produces new decisions based on past cases.Advocates claim that the process can capture the experiences of experts and alleviate problems of knowledge acquisition from human experts in rule-based systems.Do you agree? Why or why not?
1. Machine learning is a discipline that investigates how computers can learn from existing data. Scholars disagree about whether machines can really learn.Some insist that computers do not learn and are only taught by humans. Do you agree? Why or why not?
6. Describe the capability of Evolver as an optimization tool.
5. Describe in detail three genetic algorithm applications.
4. List major areas of genetic algorithm application.
3. Describe the major genetic algorithm operators.
2. Describe the evolution process in genetic algorithms. How is it similar to biological evolution?
1. Define genetic algorithm.
5. List some business applications of fuzzy logic.
4. List some nonbusiness applications of fuzzy logic.
3. What are the major advantages of fuzzy logic? What are its major limitations?
2. What are the basic premises behind the fuzzy logic approach?
1. Define fuzzy logic and describe its characteristics.
5. List and discuss some applications of SVM.
4. What are the major advantages of SVM? What are its major limitations?
3. What are the steps in building SVM solutions?
2. How does SVM work? What types of problems can SVM be applied to?
1. Define support vector machines and describe their characteristics.
5. Describe the differences between software agents and intelligent agents.
4. List and briefly comment on different categories of agents.
3. Describe the major components of intelligent agents.
2. What are the main features of intelligent agents?
1. Define intelligent agents. Why are there so many different definitions and names for intelligent agents?
3. What are the pros and cons of integrating intelligent systems?
2. Describe how genetic algorithms can be integrated with other intelligent methods and provide a sample application.
1. What is a fuzzy neural network? Give an example of an application of a fuzzy neural network.
6. In order to reduce and potentially eliminate illiteracy via automated means, it is necessary to understand what makes learning to read an easy and effective process. To date, experts believe that guided oral reading greatly enhances word identification and comprehension in context.Ample evidence
5. List five machine-learning methods.
4. List several techniques for supervised learning and unsupervised learning.
3. What are the differences between supervised learning and unsupervised learning?
2. What is learning?
1. Define machine learning.
5. List three success factors of CBR.
4. Comment on the current and future usability of CBR.
3. List five benefits of CBR.
2. Describe the four steps of the CBR process.
1. What is CBR? What problems is CBR most suitable for?
4. What do you think are the characteristics of an expert reading tutor that should be closely represented in an automated computer tool like the reading tutor?
3. What types of machine-learning technologies can be used in improving reading skills?
2. How can information systems help in overcoming illiteracy?
1. What is literacy and why does it have a global importance?
4. Find a proper application outside the financial services domain (such as manufacturing or retailing)and evaluate whether a rule-based system will generate the same level of benefits as those in the Farm Bureau system and explain why.Financial service is a major area for ES because the process
3. Evaluate potential tools for implementing such a rule-based system and list the criteria that you would use in tool selection.Financial service is a major area for ES because the process involves a number of complicated rules.Farm Bureau is a 60-year-old comprehensive financial services provider
2. Describe the benefits of using the rule-based system at Farm Bureau and explain why the system can generate these benefits.Financial service is a major area for ES because the process involves a number of complicated rules.Farm Bureau is a 60-year-old comprehensive financial services provider
1. Describe the role of business rules at Farm Bureau and explain why those rules are very important.Financial service is a major area for ES because the process involves a number of complicated rules.Farm Bureau is a 60-year-old comprehensive financial services provider that offers a broad range
5. Search online to find a few ES development tools not listed in the chapter. Classify them into different categories.
4. Access the Web site of the American Association for Artificial Intelligence (aaai.org). Examine the workshops it has offered over the past year and list the major topics related to intelligent systems.
3. Search the Internet using the keyword expert systems and describe what you find. List the first five applications in your search results and compare their differences.
2. In 1995, there were about 2,000 Web sites related to AI. Today, there are substantially more. Do a search at Google and describe how many Web sites you find.Categorize the first 20 into groups, or if you used a search engine that grouped them, what groups did you find?
1. Go to exsys.com to play with the restaurant selection example in its demo systems. Analyze the variables and rules contained in the example’s knowledge base.
3. Identify and interview an expert who knows the domain of your choice. Ask the expert to write down his or her knowledge. Choose an ES shell and build a prototype system to see how it works.
2. Search to find applications of artificial intelligence and ES.Identify an organization with which at least one member of your group has a good contact who has a decisionmaking problem that requires some expertise (but is not too complicated). Understand the nature of its business and identify
1. Download Exsys’ Corvid tool for evaluation. Identify an expert (or use one of your teammates) in an area where experience-based knowledge is needed to solve problems, such as buying a used car, selecting a school and major, selecting a job from many offers, buying a computer, diagnosing and
2. We list 10 categories of ES applications in the chapter. Find 20 sample applications, 2 in each category, from the various functional areas in an organization (i.e., accounting, finance, production, marketing, and human resources).
1. Go to teradatastudentnetwork.com and search for stories about Chinatrust Commercial Bank’s (CTCB’s) use of the Teradata Relationship Manager and its reported benefits. Study the functional demo of the Teradata Relationship Manager to answer the following questions:a. What functions in the
10. Given the current status of the Web, discuss how it is changing the availability of ES and how it is being used to embed expertise in other systems.
9. An ES for stock investment is developed and licensed for$1,000 per year. The system can help identify the most undervalued securities on the market and the best timing for buying and selling the securities. Will you order a copy as your investment advisor? Explain why or why not.
8. Review the limitations of ES discussed in this chapter.From what you know, which of these limitations are the most likely to still be limitations in the year 2100? Why?
7. What kind of mistakes might ES make and why? Why is it easier to correct mistakes in ES than in conventional computer programs?
6. Explain the difference between forward chaining and backward chaining and describe when each is most appropriate.
5. Explain the relationship between the development environment and the consultation (i.e., runtime) environment.
4. Explain how the Web improves the benefit–cost ratio of ES and enables systems that otherwise are not justifiable.
3. It is said that powerful computers, inference capabilities, and problem-solving heuristics are necessary but not sufficient for solving real problems. Explain.
2. Do you agree with the statement that using speech communication as the user interface could increase people’s willingness to use ES? Why or why not?
1. Compare numeric and symbolic processing techniques and give an example to illustrate their differences.
3. Describe the critical success factors of ES.
2. Describe some of the limitations of ES.
1. Describe the major benefits of using ES.
3. Visit exsys.com and run two of the demo systems. Describe each system.
2. How can an ES help a decision maker use the Web to find relevant information?
1. What are the benefits of deploying an ES on the Web?
3. Describe a sample ES application for the rest of the generic ES categories.
2. Describe a sample ES application for diagnosis.
1. Describe a sample ES application for prediction.
5. What is the difference between verification and validation of an ES?
4. List the criteria for choosing a development tool.
3. Compare three different types of tools for developing ES.
2. What are the necessary conditions for a good expert?
1. Describe the major steps in developing rule-based ES.
6. What are the ways by which ES justify their knowledge?
5. Why do we need to incorporate uncertainty in ES solutions?
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