1. Artificial Intelligence and Expert Systems

2. ARTIFICIAL INTELLIGENCE (AI)
is the science of R L
Being able to
Ability to solve a problem

3. Comparing a DSS to Artificial Intelligence
Decision Support System (DSS)
User actively involved with the system.
Relies on The user must understand problem situation and what needs to be done.
The user makes the ultimate decision/choice.
Artificial Intelligence
User not as actively involved because all of the expertise is
The system makes the ultimate decision/choice.

4. Robotics

5. E. Systems are that imitate the reasoning process of experts
E Systems are that imitate the reasoning process of experts. They consist of a knowledge base and a set of rules for applying that knowledge base to a particular situation. Most common form of AI in business.
Neural Networks mimic the way the brain works, analyzing large quantities of data and information to establish
Genetic algorithms mimic the evolutionary, survival-of-the-fittest process to increasingly Genetic algorithms work to find the answer.
Intelligence agents accomplish a specific task for the user.

6. AN EXPERT SYSTEM
is an artificial intelligence system that applies to reach a conclusion.
An expert system captures expertise from a human expert and applies it to a problem.
Tricks of the trade Knowledge base
Reasoning Process

7. Expert Systems Programming is in the form of and Reasons
Decision Support System guides you, but you must reason through the problem.
Expert Systems : you provide the facts, it
Used as diagnostic and prescriptive.

9. Example of Medical Expert System for lung cancer treatment
Expert System Rules for a Bank Mortgage Application
Example of Medical Expert System for lung cancer treatment
If lung capacity is high
AND X-ray results are positive
AND patient has fever
AND patient has coughing
THEN surgery is necessary.
If tumor has spread
OR contraindications to surgery exist
THEN surgery cannot be performed

10. Traffic Light Expert System

11. Expert Systems
Expert Systems are computerized advisory programs that imitate the reasoning process of experts. They consist of a knowledge base and a set of rules for applying that knowledge base to a particular situation.
EXPERT SYSTEMS
The system uses IF statements and user answers to questions in order to reason just like a human does.
It takes something the users doesn’t know and applies rules to indicate what to do.
Expert Systems: to determine what is “known.”

12. Easy Diagnosis Medical Expert System

13. WHAT EXPERT SYSTEMS CAN DO
Can handle massive amounts of information and they can
Can from complex relationships
Can explain their reasoning or suggested decisions
Provide in decision making.
Improve customer service.
Reduce errors and costs.
Provide

14. WHAT EXPERT SYSTEMS CAN’T DO
Handle all types of domain expertise. Human experts might not fully be aware of the process that they use. Can’t put everything into machine form.
Can’t solve problems in areas not designed for. Can’t
Apply or judgment to a problem

15. Acquired Intelligence Whale Watcher Douglas Fir Cone and Seed
Expert Systems Perform and Tasks Like
Expert System used by American Express’ Optima Card program.
Auditing and tax planning
Diagnosing illnesses
Managing forest resources
Evaluate credit and loan applications
Computer help desk diagnosis assistance
Rules to follow when directing air traffic
VB Loan System
Smartflow
Exsys Corvid
Which Dog Breed is best for you?
Marathon Race Advisor
Albuquerque Restaurant Advisor
Web Support
Camcorder Selection
Acquired Intelligence Whale Watcher Douglas Fir Cone and Seed

16. Ethical Questions and the Use of Expert Systems
An expert system will act as it is programmed. If you program in bias, then the system will be biased.
The expert system is consistent, which is easily defended in court.
Can distinguish between good and bad, but may not be able to distinguish between degrees of good.

17. Expert Systems are computerized advisory programs that imitate the reasoning process of experts.
EXPERT SYSTEMS apply rules to solve a problem.
Expert Systems: ask a series of questions to determine what is “known.”
Neural Networks mimic the way the brain works, analyzing large quantities of data and information to establish patterns and infer relationships.
They
They can “see” subtle, hidden and newly emerging patterns within large amounts of complex data.

18. A NEURAL NETWORK
is an artificial intelligence system which is capable of learning because it’s patterned after the human brain. Uses parallel processors.
A neural network simulates the human ability to classify things based on the experience of seeing many examples.
Learn by

19. NEURAL NETWORKS
Typically used to combat attempts at fraud
Credit card fraud or insurance fraud.
Able to detect money laundering attempts.
Working in conjunction with X-ray machines, can be used to detect weapons and other forbidden items.
Often used to make investment decisions (stocks, bonds, futures markets, etc.)
Can also detect inefficiencies in financial markets
Learn by looking at a data set and finding patterns in it.

20. A Neural Network Can Perform Tasks Like
Distinguishing different chemical compounds
D in human tissue that may signify disease
A to detect forgeries. De
Track habits of insurance customers and predict which ones might not renew their policies
Virus Detection Software by IBM
Neugent monitors 1,200 data points in the Allstate Insurance network every 5 seconds, trying to predict a potential problem in/with the network.

21. Neural networks attempt to mimic the structure and functioning of the human brain. They contain input, output and hidden layers. The hidden layers use various weights of strength to As the system , it can change the classification weights.
Neural networks can adjust or change themselves over time based upon data input regarding successful and unsuccessful mortgage applications. Neural networks as they “learn”. Expert systems

22. Neural Networks serve as Systems
Allows the computer to or it receives.
There are computer games with learning abilities.
20Questions
F and neural networks are often combined to express complicated and concepts (that are and ambiguous) in a form that makes it possible to simplify the problem and apply rules with some degree of certainty.

23. Fuzzy Logic
Fuzzy Logic: a special field of computer science that and does not require conditions to be
A mathematical method of handling information so that ambiguous information such as “ ” or “ ” or other “non-exact areas usable in computer systems
Applications
Google’s search engine (your perception of a topic frames your query)
Washing machines that wash until the water is “clean”
A and subway/tram control systems
A cameras
Temperature sensors attached to furnace controls
Medical equipment that based upon patient vital signs.
Accounting: how do you value intangible assets such as

24. EXPERT SYSTEMS apply rules to solve a problem.
The system uses IF statements and user answers to questions in order to reason just like a human does.
It takes something the users doesn’t know and applies rules to indicate what to do.
Expert Systems: ask a series of questions to determine what is “known.”
NEURAL NETWORKS recognize/learn patterns and can apply that learning to the unknown.
It is either taught by someone or teaches itself. After it is taught to recognize the pattern, it can adjust itself to reflect new learning.
Neural networks: system is “guessing” based upon examples and patterns found in the data set- trying to figure out what category something fits in.
GENETIC ALGORITHMS generate several generations of solutions, with each generation resulting in a to the problem.

25. A GENETIC ALGORITHM
is an artificial intelligence system that mimics the to generate increasingly better solutions to a problem.
Genetic algorithms produce several generations of solutions, choosing the best of the current set for each new generation.

26. THE CONCEPTS OF EVOLUTION IN GENETIC ALGORITHMS
- or survival of the fittest. The key is to give preference to better outcomes.
- combining portions of good outcomes in the hope of creating an even better outcome.
- randomly trying combinations and evaluating the success (or failure) of the outcome.
Seeking an

27. Genetic Algorithms Can Generate Lots of Solutions As In
Deciding which a firm should invest in, given limited investment dollars.
Generating solutions to
How much cable or track to lay?
What should your delivery vehicles take?
Used to (make the best use of your production resources)
Investment companies use them to generate by considering of stocks and bonds .
Clothing manufacturing: so as to generate the
The Traveling Salesman

28. AN INTELLIGENT AGENT
is a that and then with a certain degree of , and in doing so, employs knowledge or representation of the user’s goals or desires.
The Agent will take your profile and preferences and then go out and work on your behalf.
Characteristics of an intelligent agent A : can act without you telling them what to do A : can and what it does based upon your changing characteristics. S : can and with other agents that it encounters.

29. Types of Intelligent Agents
I and bring it back to you (from the Internet or a database)
B s, shopping bots, , Googlebots that scour the Internet locating and indexing sites that ultimately appear in search results when you do a Google search.
Information agents for Amazon display lists of books and other products that customers might like, based on past purchases.
M and Surveillance Agents: constantly
A and offer suggestions for improvement.
Agents that monitor web sites for updated info, such as price changes on desired products.
Wizards in Microsoft Office
U : act as a personal assistant by Examples include sorting and prioritizing , filling out forms on the Web automatically for you, and automatically storing your information.
D agents operate in a data warehouse by sifting through the data, trying to discover trends, relationships and patterns through the use of multidimensional statistical analysis.

30. Monitoring & Surveillance Agents: constantly observe and report back on what they see.
Spell Checker
Grammar Checker
Monitoring and surveillance agent in Excel

31. Data-mining agents perform multidimensional analysis in data warehouses
Cube – common term for the representation of multi-dimensional information (layers, rows, columns)
Users can slice and dice the cube to drill down into the information
Cube A represents store information (the layers), product information (the rows), and promotion information (the columns)
Cube B represents a slice of information displaying promotion II for all products at all stores
Cube C represents a slice of information displaying promotion III for product B at store 2
CLASSROOM EXERCISE
Analyzing Multiple Dimensions of Information
Jump! is a company that specializes in making sports equipment, primarily basketballs, footballs, and soccer balls. The company currently sells to four primary distributors and buys all of its raw materials and manufacturing materials from a single vendor. Break your students into groups and ask them to develop a single cube of information that would give the company the greatest insight into its business (or business intelligence) given the following choices:
Product A, B, C, and D
Distributor X, Y, and Z
Promotion I, II, and III
Sales
Season
Date/Time
Salesperson Karen and John
Vendor Smithson
Remember you can pick only 3 dimensions of information for the cube, they need to pick the best 3
Product
Promotion
These give the three most business-critical pieces of information

32. EXPERT SYSTEMS apply rules to solve a problem.
The system uses IF statements and user answers to questions in order to reason just like a human does.
It takes something the users doesn’t know and applies rules to indicate what to do.
NEURAL NETWORKS recognize/learn patterns and can apply that learning to the unknown.
It is either taught by someone or teaches itself. After it is taught to recognize the pattern, it can adjust itself to reflect new learning.
GENETIC ALGORITHMS generate several generations of solutions, with each generation resulting in a better solution to the problem.

33. Expert Systems: ask a series of questions to determine what is “known
Neural networks: system is “guessing” based upon examples and patterns found in the data set- trying to figure out what category something fits in.

34. Starting Information
Expert’s know-how
Acceptable patterns
Set of possible solutions
Your preferences
AI System
Expert Systems
Neural Networks
Genetic Algorithms
Intelligent Agents
Problem Type
Diagnostic or prescriptive
Identification, classification, prediction
Optimal solution
Specific and repetitive tasks
Based On
Strategies of experts
The human brain
Biological evolution
One or more AI techniques