Artificial Intelligence

and Expert Systems

ARTIFICIAL INTELLIGENCE (AI)is the science of

• R

• L

• Being able to

• Ability tosolve a problem

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.

Robotics

• 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.

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

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.

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

Traffic Light Expert System

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.”

Easy Diagnosis Medical Expert System

• 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

WHAT EXPERT SYSTEMS CAN DO

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

• 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

Acquired IntelligenceWhale Watcher

Douglas Fir Cone and Seed

Smartflow

Expert System used by American Express’ Optima Card program.

VB Loan System

Exsys CorvidWhich Dog Breed is best for you?Marathon Race AdvisorAlbuquerque Restaurant AdvisorWeb SupportCamcorder Selection

Expert Systems Perform and Tasks Like

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.

• 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.

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

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.

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.

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 .

Neural Networks serve as Systems• Allows the computer to or

it receives.

• There are computer games with learning abilities.• 20Questions www.20Q.net

• 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.

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

• 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.

A GENETIC ALGORITHMis 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.

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

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

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AN INTELLIGENT AGENTis a that and thenwith 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 agentA : can act without you telling them what to doA : can and what it does based upon your changing characteristics.S : can and with other agents that it encounters.

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 email, 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.

Monitoring & Surveillance Agents: constantly observe and report back on what they see.

• Spell Checker • Grammar Checker

• Monitoring andsurveillance agentin Excel

Data-mining agents perform multidimensional analysis in data

warehouses • Cube – common term for the representation of multi-

dimensional information (layers, rows, columns)

• 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.

• 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.

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

Starting Information

Expert’s know-how

Acceptable patterns

Set of possible solutions

Your preferences