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INTELLIGENT INFORMATION SYSTEMS

MIS. CHAPTER 13. INTELLIGENT INFORMATION SYSTEMS. Hossein BIDGOLI. Chapter 13 Intelligent Information Systems. l e a r n i n g o u t c o m e s. LO1 Define artificial intelligence and explain how these technologies support decision making.

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INTELLIGENT INFORMATION SYSTEMS

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  1. MIS CHAPTER 13 INTELLIGENT INFORMATION SYSTEMS Hossein BIDGOLI

  2. Chapter 13 Intelligent Information Systems l e a r n i n g o u t c o m e s LO1Define artificial intelligence and explain how these technologies support decision making. LO2Explain an expert system, its applications, and its components. LO3Describe case-based reasoning. LO4Summarize types of intelligent agents and how they’re used. LO5 Describe fuzzy logic and its uses.

  3. Chapter 13 Intelligent Information Systems l e a r n i n g o u t c o m e s (cont’d.) LO6Explain artificial neural networks. LO7Describe how genetic algorithms are used. LO8Explain natural language processing and its advantages and disadvantages. LO9Summarize the advantages of integrating AI technologies into decision support systems.

  4. What Is Artificial Intelligence? • Artificial intelligence (AI) • Consists of related technologies that try to simulate and reproduce human thought and behavior • Includes thinking, speaking, feeling, and reasoning • AI technologies • Concerned with generating and displaying knowledge and facts

  5. What Is Artificial Intelligence? (cont’d.) • Knowledge engineers try to discover “rules of thumb” • Enable computers to perform tasks usually handled by humans • Capabilities of these systems have improved in an attempt to close the gap between artificial intelligence and human intelligence

  6. AI Technologies Supporting Decision Making • Decision makers use information technologies in decision-making analyses: • What-is • What-if • Other questions: • Why? • What does it mean? • What should be done? • When should it be done?

  7. Table 13.1 Applications of AI Technologies

  8. Robotics • Some of the most successful applications of AI • Perform well at simple, repetitive tasks • Currently used mainly on assembly lines in Japan and the United States • Cost of industrial robots • Some robots have limited vision

  9. Robotics (cont’d.) • Honda’s ASIMO • One of the most advanced and most popular robots • Works with other robots in coordination • Personal robots • Mobility, limited vision, and some speech capabilities • Robots have some unique advantages in the workplace compared with humans

  10. Expert Systems • One of the most successful AI-related technologies • Mimic human expertise in a field to solve a problem in a well-defined area • Consist of programs that mimic human thought behavior • In a specific area that human experts have solved successfully • Work with heuristics

  11. Components of an Expert System • Knowledge acquisition facility • Knowledge base • Factual knowledge • Heuristic knowledge • Meta-knowledge • Knowledge base management system (KBMS) • Explanation facility • Inference engine

  12. Exhibit 13.1 An Expert System Configuration

  13. Components of an Expert System (cont’d.) • Forward chaining • Series of “If-Then-Else” • Condition pairs are performed • “If” condition is evaluated first • Then the corresponding “Then-Else” action is carried out • Backward chaining • Starts with the goal first—the Then part • Backtracks to find the right solution

  14. Components of an Expert System (cont’d.) • Semantic (associative) networks • Represent information as links and nodes • Frames • Store conditions or objects in hierarchical order • Scripts • Describe a sequence of events

  15. Uses of Expert Systems • Airline industry • Forensics lab work • Banking and finance • Education • Food industry • Personal management • Security • US Government • Agriculture

  16. Expert Systems in Baltimore County Police Department • In Baltimore County, an expert system was developed so that detectives could analyze information about burglary sites and identify possible suspects • Detectives could enter statements about burglaries, such as neighborhood characteristics, the type of property stolen, and the type of entry used; they could also get information on possible suspects

  17. Criteria for Using Expert Systems • Human expertise is needed but one expert can’t investigate all the dimensions of a problem • Knowledge can be represented as rules or heuristics • Decision or task has already been handled successfully by human experts • Decision or task requires consistency and standardization

  18. Criteria for Using Expert Systems (cont’d.) • Subject domain is limited • Decision or task involves many rules and complex logic • Scarcity of experts in the organization

  19. Criteria for Not Using Expert Systems • Very few rules • Too many rules • Well-structured numerical problems are involved • Problems are in areas that are too wide and shallow • Disagreement among experts • Problems are solved better by human experts

  20. Advantages of Expert Systems • Never becomes distracted, forgetful, or tired • Duplicates and preserves the expertise of scarce experts • Preserve the expertise of employees who are retiring or leaving an organization • Creates consistency in decision making • Improves the decision-making skills of nonexperts

  21. Case-Based Reasoning • Problem-solving technique • Matches a new case (problem) with a previously solved case and its solution stored in a database • If there’s no exact match between the new case and cases stored in the database: • System can query the user for clarification or more information • If still no match found: • Human expert must solve the problem

  22. Intelligent Agents • Bots (short for robots) • Applications of artificial intelligence are becoming more popular • Particularly in e-commerce • Consist of software capable of reasoning and following rule-based processes

  23. Intelligent Agents (cont’d.) • Characteristics: • Adaptability • Autonomy • Collaborative behavior • Human-like interface • Mobility • Reactivity

  24. Intelligent Agents (cont’d.) • Web marketing • Collects information about customers, such as items purchased, demographic information, and expressed and implied preferences • “Virtual catalogs” • Display product descriptions based on customers’ previous experiences and preferences

  25. Shopping and Information Agents • Help users navigate through the vast resources available on the Web • Provide better results in finding information • Examples: • PriceScan • BestBookBuys.com • www.mysimon.com • DogPile • Searches the Web by using several search engines • Eliminates duplicate results

  26. Personal Agents • Agents perform specific tasks for a user • Such as: • Remembering information for filling out Web forms • Completing e-mail addresses after the first few characters are typed

  27. Data-Mining Agents • Work with a data warehouse • Detect trend changes • Discover new information and relationships among data items that aren’t readily apparent • Having this information early enables decision makers to come up with a solution that minimizes the negative effects of the problem

  28. Monitoring and Surveillance Agents • Track and report on computer equipment and network systems • To predict when a system crash or failure might occur • Example: NASA’s Jet Propulsion Laboratory

  29. Fuzzy Logic • Allows a smooth, gradual transition between human and computer vocabularies • Deals with variations in linguistic terms by using a degree of membership • Designed to help computers simulate vagueness and uncertainty in common situations • Works based on the degree of membership in a set

  30. Exhibit 13.3 Degree of Membership in a Fuzzy System

  31. Uses of Fuzzy Logic • Used in: • Search engines, chip design, database management systems, software development, and more • Examples: • Dryers • Refrigerators • Shower systems • TVs • Video camcorders

  32. Artificial Neural Networks • Networks that learn and are capable of performing tasks that are difficult with conventional computers • Examples: • Playing chess • Recognizing patterns in faces • Used for poorly structured problems • Use patterns instead of the “If-Then-Else” rules that expert systems use • Create a model based on input and output

  33. Exhibit 13.4 An Artificial Neural Network Configuration

  34. Artificial Neural Networks (cont’d.) • Used for many tasks, including: • Bankruptcy prediction • Credit rating • Investment analysis • Oil and gas exploration • Target marketing

  35. Neural Networks in Action • Many companies are able to predict customers’ shopping behavior based on past purchases • E-banks use neural networks to rank their customers into groups • Visa International • Introduced a credit authorization system based on neural networks to reduce credit card fraud • Could cut fraudulent transactions by as much as 40%

  36. Genetic Algorithms • Used mostly in techniques to find solutions to optimization and search problems • Applications: • Jet engine design, portfolio development, and network design • Find the combination of inputs that generates the most desirable outputs • Techniques: • Selection or survival of the fittest • Crossover • Mutation

  37. Natural Language Processing • Developed so that users can communicate with computers in their own language • Provides question-and-answer setting that’s more natural and easier for people to use • Products aren’t capable of a dialogue that compares with conversations between humans • However, progress has been steady

  38. Table 13.2 NLP Systems

  39. Natural Language Processing (cont’d.) • Categories: • Interface to databases • Machine translation • Text scanning and intelligent indexing programs for summarizing large amounts of text • Generating text for automated production of standard documents • Speech systems for voice interaction with computers

  40. Natural Language Processing (cont’d.) • Interfacing • Accepting human language as input • Carrying out the corresponding command • Generating the necessary output • Knowledge acquisition • Using the computer to read large amounts of text and understand the information well enough to: • Summarize important points and store information so that the system can respond to inquiries about the content

  41. Integrating AI Technologies into Decision Support Systems • I-related technologies can improve the quality of decision support systems (DSSs) • Including expert systems, natural language processing, and artificial neural networks • Benefits of integrating an expert system into the database component of a DSS are: • Adding deductive reasoning to traditional DBMS functions • Improving access speed

  42. Summary • Intelligent information systems • AI technologies are used to support decision-making processes • Expert systems • Components • Case-based reasoning • Intelligent agents • Fuzzy logic and genetic algorithms • Natural language processing

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