1. Specialized Business Information Systems
Chapter 7
Chapter 11 discusses specialized information systems used in business, including artificial intelligence, or AI, expert systems, and virtual reality. After studying this chapter, you should be able to address the objectives on the next 3 slides.
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2. The Nature of Intelligence
Learn from experience & apply the knowledge
Deep Blue improves its performance by playing with humans
Handle complex situations
Traffic problem in Istanbul
Solve problems when important information is missing
Based on available information
Determine what is important
Choose which facts to use to compute the solution
Humans naturally learn from experience – that is, by trial and error. And humans can apply what they learn to different contexts. Neither trait comes naturally to AI systems – they can only learn and apply what they’ve been programmed to learn – and the programming is difficult. Humans can learn in multiple areas and automatically apply what they learn.
Humans are often involved in complex situations. In business, executives face complicated legislation, rapidly changing markets and competition, and many more complexities, yet must make decisions, sometimes quickly, that affect their company’s future. People can make mistakes and learn from them. However, computers can handle only those complex situations that they’re programmed to handle.
Humans continually make decisions under uncertainty – that is, with partial or even inaccurate, information. AI systems can handle such situations in many contexts.
Everyday humans receive masses of incoming information. People can screen the information and discard irrelevant information – a skill built through experience. Computers are limited by their programming – and it’s not easy to program a computer to know what’s irrelevant.
Fundamentals of Information Systems, Second Edition

3. The Nature of Intelligence
React quickly and correctly to new situations
Requires understanding the new situation
Understand visual images
Requires perception
Process and manipulate symbols
Computers are better at dealing with numbers
Be creative and imaginative
Use heuristics
Rules of thumb from experience
Humans experience “gut instincts” – like when you walk down a street and “know” you should leave or get hurt. Children know not to touch a flame. Computers have no gut feelings, and can only react quickly to specific stimuli for which they’re programmed.
Even state of the art computers have trouble interpreting visual images. When a person sees their reflection in the mirror, they know it’s a reflection and not a clone. When you see people, you look for many clues in dress, grooming, and behavior to determine their gender. Walking down a crowded street is natural even for children. But none of this is natural to computers. Research in the area of perceptive systems – that is, machines that can mimic human hearing, sight, or touch – has progressed and, as we’ll see later in the chapter, some systems have limited recognition ability.
Although computers excel at rapidly processing numbers, they’re not so good at processing visual information. Again, they’re limited by their programming.
Humans can think of new products and services and create novel objects. Although computers have been used to write poetry or draw, few can yet be considered truly creative.
Heuristics are rules of thumb developed through experience. People often use heuristics in decision-making. For instance, if you leave home for work after 7:30 AM, you may choose an alternate route, since experience has shown that by then there is often an accident backing up traffic on your normal route. Or you may ignore the weather forecast of precipitation if the chance is less than 70%, because based on your experience, it rarely rains or snows unless the chance is 70% or higher.
Fundamentals of Information Systems, Second Edition

4. A comparison of Natural and Artificial Intelligence
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5. A Conceptual Model of Artificial Intelligence
Fundamentals of Information Systems, Second Edition

6. What is an Expert System?
Hardware and software that contain knowledge and manipulate knowledge by inferences
Mycin (Shortliffe 1976): Expert system for medicine
Program for advising physicians on treating bacterial infections
Question and answer dialogues with user
Accounts for uncertainties
Explains its reasoning
An expert system can explain how it reached a conclusion by showing the path of rules and inferences in its knowledge base that it followed. This is valuable to users of the conclusions. For instance, a physician using an expert system to help diagnose a blood disease could compare the expert system’s reasoning to her own to determine her level of confidence in the system’s conclusion. This is also useful in training novices in an area. For example, a new loan processor making a decision to approve or deny a loan can see the expert system’s reasoning and learn from it. Because an expert’s knowledge is codified in an expert system, expert systems can preserve scarce expertise and give others access to it.
Given a data set, an expert system can propose new ideas, which is a characteristic of expert behavior. For example, expert systems can diagnose patients’ conditions from their symptoms or suggest where to drill for oil, based on geologic data and expert knowledge.
Expert systems can evaluate complex relationships to reach a conclusion or make a recommendation. Although expert systems generally require a well-structured problem, it can have many complex relationships. The information can be incomplete or somewhat inaccurate, since expert systems can use probabilities and heuristics.
Fundamentals of Information Systems, Second Edition

7. Characteristics of an Expert System
Can explain their reasoning or suggested decisions
Why recommend a certain medicine?
Can display “intelligent” behavior
Can draw conclusions from complex relationships
A patient is diagnosed with two diseases,
The cures for the diseases may have conflicts
Can provide portable knowledge
Capture knowledge in one’s brain
Can deal with uncertainty
A patient is diagnose without running all the tests
An expert system can explain how it reached a conclusion by showing the path of rules and inferences in its knowledge base that it followed. This is valuable to users of the conclusions. For instance, a physician using an expert system to help diagnose a blood disease could compare the expert system’s reasoning to her own to determine her level of confidence in the system’s conclusion. This is also useful in training novices in an area. For example, a new loan processor making a decision to approve or deny a loan can see the expert system’s reasoning and learn from it. Because an expert’s knowledge is codified in an expert system, expert systems can preserve scarce expertise and give others access to it.
Given a data set, an expert system can propose new ideas, which is a characteristic of expert behavior. For example, expert systems can diagnose patients’ conditions from their symptoms or suggest where to drill for oil, based on geologic data and expert knowledge.
Expert systems can evaluate complex relationships to reach a conclusion or make a recommendation. Although expert systems generally require a well-structured problem, it can have many complex relationships. The information can be incomplete or somewhat inaccurate, since expert systems can use probabilities and heuristics.
Fundamentals of Information Systems, Second Edition

8. Characteristics of an Expert System
Not widely used or tested
Limited to relatively narrow problems
Cannot readily deal with “mixed” knowledge
Expert systems should talk to each other
Cannot refine its own knowledge
Should be able to keep a consistent knowledgebase
Should have a way to gain new knowledge
May have high development costs
Raise legal and ethical concerns
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9. When to Use Expert Systems
High payoff
Preserve scarce expertise
Provide more consistency than humans
Faster solutions than humans
Training expertise
Since expert systems can be difficult and expensive to develop, they should be used where they can be most beneficial. This slide summarizes situations where expert systems have been shown to be worth implementing.
Clearly, when there is a high potential payoff, or when the expertise is needed at a place dangerous to humans, it makes sense to develop the expert system.
It is generally also worthwhile to develop an expert system to capture and preserve expertise that not many people have, that is expensive, or that can’t be duplicated in other ways. Also, an expert system is called for when this kind of scarce expertise is needed in many locations at once.
No matter how hard they try, people cannot be 100% consistent – they tire, have bad moods, or are distracted. Where consistency is needed – say in loan approval – investing in an expert system may be worthwhile.
In complex tasks, such as configuring large computer installations, it may take humans too long to do the job for the company to be competitive. Using an expert system to complete the task quicker than your competition would be wise.
And finally, sharing scarce expertise or training others in the area, is a solid use of expert systems.
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10. Components of an Expert System
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11. The Relationships Among Data, Information, and Knowledge
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12. Rules for a Credit Application
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13. The Knowledge Acquisition Facility
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14. Fundamentals of Information Systems, Second Edition
Knowledge Base
Assembling human experts
Combine knowledge from several experts
Disagree on many items
The use of fuzzy logic
For relations that are not precise
Is a 50-year old man old?
Help computers deal with imprecise knowledge
Ex: Washing machines; Auto-focus cameras
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15. Fundamentals of Information Systems, Second Edition
Knowledge Base
The use of rules
Rule: Conditional statement (if … then)
If the condition matches, the action fires
More rules generally mean more precision
The use of cases
Template of problems or situations
To find the solution of a new case, find similar old cases and apply result
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16. Fundamentals of Information Systems, Second Edition
Inference Engine (1)
Use information and relations to derive new facts to solve problems or predict possible outcomes
Main reasoning component
Find the right facts, apply the right relations, etc.
Ex: Facts: male(Ali), female(Oya)
Relations: father(X, Y) => male(X)
The engine can conclude that Oya cannot be a father.
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17. Fundamentals of Information Systems, Second Edition
Inference Engine (2)
Backward chaining
You start with conclusions
You want to find out if you can get to the conclusion from your facts
Forward chaining
You start with facts and try to reach conclusions
More expensive since it can generate many conclusions
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18. Fundamentals of Information Systems, Second Edition
Other Components
Explanation Facility
Enables the expert system to explain its reasoning
Helps the user to judge the expert system
Knowledge Acquisition Facility
Get and update knowledge
Provide a way to capture and store knowledge
Can be semi-automated
User Interface
Help users interact with the system
Improve usability
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19. Expert Systems Development
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20. Participants in Developing and Using Expert Systems
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21. Fundamentals of Information Systems, Second Edition
Domain Expert
Recognize the real problem
Develop a general framework for problem solving
Formulate theories about the situation
Develop and use general rules to solve a problem
Know when to break the rules or general principles
Solve problems quickly and efficiently
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22. Fundamentals of Information Systems, Second Edition
Other participants
Knowledge Engineer
Works in design and implementation of the expert system
Has considerable information about expert systems
Knowledge User
End user who will benefit from the system
No need to know anything about expert systems
Can help in testing
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23. Expert Systems Development Alternatives
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24. Applications of Expert System and Artificial Intelligence
Credit granting and loan analysis
Stock Picking
Catching cheats and terrorists
NORA (Non-obvious Relationship Awareness)
Budgeting
Fundamentals of Information Systems, Second Edition

25. Applications of Expert System and Artificial Intelligence
Games: Proverb solves crossword puzzles
Writing: Evaluate and rate writings
Information management and retrieval
Virus detection
Learns the actions of a virus
Hospitals and medical facilities
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26. Fundamentals of Information Systems, Second Edition
Virtual Reality
Enables one or more users to move and react in a computer-simulated environment
Immersive virtual reality - user becomes fully immersed in an artificial, three-dimensional world that is completely generated by a computer
Virtual reality system - enables one or more users to move and react in a computer-simulated environment
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27. Fundamentals of Information Systems, Second Edition
Interface Devices
Head mounted display (HMD)
Binocular Omni-Orientation Monitor (BOOM)
CAVE
Fundamentals of Information Systems, Second Edition

28. The BOOM, a Head-Coupled Display Device
Fundamentals of Information Systems, Second Edition

29. Viewing the Detroit Midfield Terminal in an Immersive CAVE System
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30. Fundamentals of Information Systems, Second Edition
Useful Applications
Medicine – used to link stroke patients to physical therapists
Education and training – used by military for aircraft maintenance
Entertainment
Star Wars Episode II: Attack of the Clones
Real Estate Marketing and Tourism
Used to increase real estate sales
Virtual reality tour of the White House
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31. Fundamentals of Information Systems, Second Edition
Segway
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32. Fundamentals of Information Systems, Second Edition
Robotics
Mechanical or computer devices that can move autonomously
Manufacturers use robots to assemble or paint products
Asimo in Istanbul: Shake hands, dance
Unmanned Combat Air Vehicles (UCAVs): Identify and destroy targets without human intervention
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Vision Systems
Capture, store, manipulate visual images
Fingerprint analysis: Store a database of fingerprints and information about the owners.
Match a fingerprint with an existing entry in the database
Mostly recognize black and white
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34. Natural Language Processing
Understand and react to statements in natural language
Three levels of understanding
Commands
Discrete
Continuous
Talk to a computer; computer converts languages to commands understandable by computers
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35. Fundamentals of Information Systems, Second Edition
Learning Systems
Change its behavior over time
Computer takes an action
User gives feedback
Based on the feedback, computer modifies its action
First train the system; then try on test data
Amazon.com learns user models as users browse and buy goods
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36. Fundamentals of Information Systems, Second Edition
Summary
Artificial intelligence - used to describe computers with ability to mimic or duplicate functions of the human brain
Intelligent behavior - includes the ability to learn from experience
Expert systems - can explain their reasoning (or suggested decisions) and display intelligent behavior
Virtual reality system - enables one or more users to move and react in a computer-simulated environment
Special-purpose systems - assist organizations and individuals in new and exciting ways
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37. Principles and Learning Objectives
Artificial intelligence systems form a broad and diverse set of systems that can replicate human decision making for certain types of well-defined problems.
Define the term artificial intelligence and state the objective of developing artificial intelligence systems.
List the characteristics of intelligent behavior and compare the performance of natural and artificial intelligence systems for each of these characteristics.
Identify the major components of the artificial intelligence field and provide one example of each type of system.
The field of artificial intelligence includes several different types of systems that replicate or mimic functions of the human brain. Although artificial systems are better at some things than are humans, humans surpass machines at others.
Fundamentals of Information Systems, Second Edition

38. Principles and Learning Objectives
Expert systems can enable a novice to perform at the level of an expert but must be developed and maintained very carefully.
List the characteristics and basic components of expert systems.
Outline and briefly explain the steps for developing an expert system.
Identify the benefits associated with the use of expert systems.
Expert systems are a type of artificial intelligence that is widely used in business. Expert systems provide novices with the capabilities of an expert.
Fundamentals of Information Systems, Second Edition

39. Principles and Learning Objectives
Virtual reality systems have the potential to reshape the interface between people and information technology by offering new ways to communicate information creatively.
Define the term virtual reality and provide three examples of virtual reality applications.
Special-purpose systems can help organizations and individuals achieve their goals.
Discuss examples of special-purpose systems for organizational and individual use.
Virtual reality systems offer a new, highly-interactive, three-dimensional interface between computers and people. Virtual reality applications have begun to spread through businesses.
Fundamentals of Information Systems, Second Edition