making decisions: artificial intelligence. artificial intelligence (ai) is the science of making...

Making Decisions:Artificial

Intelligence

ARTIFICIAL INTELLIGENCE (AI)is the science of making machines imitate

human thinking and behavior.• Reasoning

• Learning

• Being able to adapt

• Ability tosolve a problem

Comparing a DSS to Artificial Intelligence

• Decision Support System (DSS)– User actively involved with the system.– Relies on user expertise. 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 built into the system.– The system makes the ultimate decision/choice.

RoboticsA mechanical device equipped with simulated human

senses and capable of taking actions on its own.

• 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. Most common form of AI in business.

• Neural Networks mimic the way the brain works, analyzing large quantities of data and information to establish patterns and infer relationships.

• Genetic algorithms mimic the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem. Genetic algorithms work to find the best/optimal answer.

• Intelligence agents accomplish a specific task for the user.

AN EXPERT SYSTEM

is an artificial intelligence system that applies reasoning capabilities 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 Rules, If-Then

statements, and Reasons

• Decision Support System guides you, but you must reason through the problem.

• Expert Systems : you provide the facts, it solves the problem.– Recommendation from some expert systems is a

probability or other type of “score.”

• 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 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.”

Easy Diagnosis Medical Expert System

• Can handle massive amounts of information and they can provide new information.

• Can draw conclusions from complex relationships

• Can explain their reasoning or suggested decisions

• Provide consistency in decision making.

• Improve customer service.

• Reduce errors and costs.

• Provide portable knowledge

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 learn to solve new things.

• Apply common sense or judgment to a problem

Expert Systems Perform Diagnostic and Prescriptive Tasks Like

• 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

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 recognize patterns

• 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. Pattern Recognition

Learn by looking at a data set and finding patterns in it.

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 Pattern Recognition Tasks Like

• Distinguishing different chemical compounds• Detecting abnormalities in human tissue that may

signify disease Analyze travel patterns to detect potential drug smuggling.

• Analyze handwriting to detect forgeries.• Detecting credit card and insurance fraud • 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 classify and categorize things. As the system learns, 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 can adjust themselves as they “learn”. Expert systems follow fixed rules.

Neural Networks serve as Learning Systems• Allows the computer to change how it functions or

reacts to situations based on the feedback it receives.

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

• Fuzzy logic and neural networks are often combined to express complicated and subjective concepts (that are vague 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

allows for shades of gray and does not require conditions to be black/white, yes/no, or true/false.

• A mathematical method of handling imprecise or subjective information so that ambiguous information such as “short/long” or “hot/cold” 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”– Antilock brakes and subway/tram control systems – Auto focus cameras– Temperature sensors attached to furnace controls– Medical equipment that automatically makes corrections based

upon patient vital signs.

• 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 better solution to the problem.

A GENETIC ALGORITHMis an artificial intelligence system that

mimics the evolutionary, survival-of-the-fittest processes 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.

Begin running Car evolution genetic algorithm

THE CONCEPTS OF EVOLUTION IN GENETIC ALGORITHMS

• SELECTION - or survival of the fittest. The key is to give preference to better outcomes.

• CROSSOVER - combining portions of good outcomes in the hope of creating an even better outcome.

• MUTATION - randomly trying combinations and evaluating the success (or failure) of the outcome. Seeking an optimal configuration.

Genetic Algorithms Can Generate Lots of Solutions As In

• Deciding which combination of projects a firm should invest in, given limited investment dollars.

• Generating solutions to routing problems– How much cable or track to lay?– What path/route should your delivery vehicles take?

• Used to optimize production schedules (make the best use of your production resources)

• Investment companies use them to generate optimal stock/bond portfolios by considering different combinations of stocks and bonds .

• Clothing manufacturing: cutting a piece of fabric so as to generate the least amount of waste.

The Traveling Salesman

• 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.

AN INTELLIGENT AGENTis a software entity that senses its environment and

then carries out some operations on behalf of a user, with a certain degree of autonomy, 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 agentAutonomy: can act without you telling them what to doAdaptivity: can change itself and what it does based upon your changing characteristics.Sociability: can communicate and interact with other agents that it encounters.

Types of Intelligent Agents• Information Agents: search for information and bring it back to you (from the

Internet or a database)– Buyer agents, shopping bots, search and filtering agents, Googlebots that scour the

Internet locating and indexing sites that ultimately appear in search results when you do a Google search.

– The SuperFetch feature (search feature) found in Vista

• Monitoring and Surveillance Agents: constantly observe and report back on what they see.– Applications software agents monitor the activities of software users and offer

suggestions for improvement.– Wizards and the Microsoft Office Assistant (Clip It)

• User Agents: act as a personal assistant by taking action on your behalf. Examples include sorting and prioritizing email, filling out forms on the Web automatically for you, and automatically storing your information.

• Data Mining 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.

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

• A relational database stores information in a series of two dimensional tables.

• Data warehouses are multidimensional, containing layers of rows and columns. Each dimension is an attribute of information.

Data-mining agents perform multidimensional analysis in data

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

dimensional information (layers, rows, columns)

• Info in an Excel spreadsheet and a relational database (Access) appears in the form of a two dimensional table of rows and columns.

• By adding a Page Field to a Pivot Table, you can add another dimension of information: 3-D (rows and columns and layers).– Creating a 3-dimensional Pivot Table in Excel is a

means of conceptually building a data warehouse. Page fields represent the depth layer

• Pivot Tables can help you see relationships in the data

McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved

Extra Credit OpportunitiesExtra Credit

Opportunities

• This type of system is used to find the very best, or optimal answer? It is based on evolutionary concepts– The Genetic Algorithm

• What type of system is based on pattern recognition? Due to its pattern recognition abilities, it is capable of learning. It can detect forgeries and new viruses it has never seen.– The Neural Network

• This type of system can reason through a process. It is used for diagnostic and prescriptive types of tasks.– The Expert System

• This is a highly interactive system where you have to know what you are doing to effectively use it. It is used to solve non-programmed decisions (unstructured problems) where there are several possible correct answers. Spreadsheet what-if analysis is an example of what this system can do.– Decision Support System

• Data mining agents conduct multi-dimensional analysis of the data found in a ______________.– Data warehouse

• Buyer agents, shop bots and googlebots are examples of what type of agent?– Information agents

• The Microsoft Office Assistant wizard (Clip It) is an example of what type of agent?– Monitoring and surveillance agent

• As you move from lower to upper levels in an organization, information needs move from ___________ in nature to __________.– Transactional in nature to analytical

Decision support systemsTransactional decisionsAd-hoc decisionsNonprogrammed decisionsProgrammed decisionsAnalytical decisions (these are the answers)

• ____________ have well-defined relationships and are easily quantifiable while _________________ may have several answers that will work, and they are not easily quantifiable.

• If you are trying to find the very best solution given the constraints provided, you are using a technique called ___________– optimization

• If you want to find a good solution, one that satisfies all your decision criteria, without necessarily being the best solution., you are using a technique called _____________.– satisficing

The End

Natural Language Processing• Allows the computer to understand and react to

statements and commands made in a “natural” language, such as English.– Often used to replace telephone answering menu

systems. Have also been used to make dictations and text files from speech.

• Three Levels of Natural Language Processing– Command recognition– Discrete voice recognition: recognizes dictated speech

with pauses between words.– Continuous voice recognition: when a system recognizes

natural speech and understands its meaning.Jane saw a letter on the desk. She quickly read it.

Vision Systems• Used to capture, store, and retrieve visual images

and pictures. • Used in fingerprint identification, identifying

people based on facial features, robotics, and by the Post Office to sort mail

Learning Systems• Allows the computer to change how it functions or

reacts to situations based on the feedback it receives.

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

Components of Expert Systems

The Inference Engine• Processes the domain expertise and your

problem facts to reach a conclusion.– This is where the expert system does its “reasoning.”

The Knowledge Base• Stores all relevant data, information, rules,

cases, or relationships that the expert system uses. – IF THEN, ELSE statements– Cases: find cases in the knowledge base similar to

the problem or situation at hand, and then modify the solution of the existing case to fit the new/current problem or situation.

COMPONENTS OF AN EXPERT SYSTEM

• KNOWLEDGE ACQUISITION - used by the knowledge engineer to build the expert system.– Knowledge may also come from books, reports,

databases, data warehouses, diagrams, etc.

• USER INTERFACE - used to run a consultation. You interact with the system.

• EXPLANATION FACILITY - stores the “Why?” information. – Consultation: The user can ask the system to

explain the logic that underlies a question.

A Self-Organizing Neural Network: finds patterns and relationships in vast amounts of data by itself.Back-propagation neural networks are trained by someone. You teach the neural network by example.

Neural networks attempt tomimic the structure and functioning of the human brain. They contain input, output and hidden layers. The hidden layers use various weights of strength to classify and categorize things. As the system learns, it can change the classification weights.