Artificial Intelligence

Robbie NakatsuAIMS 2710

Artificial Intelligence--AI

.The techniques and software that

enable computers to mimic human behavior in various ways. A major thrust in this field is to develop computer functions associated with human intelligence.

Some Types of AI

• Expert systems • Natural language processing

• Machine learning • Robotics

• Intelligent Agents • Logic reasoning

AI is like magic!

“Any sufficiently advanced technology is indistinguishable from magic.”

--Arthur C. Clark, 1962

An Expert System

is an AI program that emulates the decision-making ability of a human expert

An expert system captures expertise from a human expert and applies it to a problem.

An Expert System can perform diagnostic and prescriptive tasks like:

Auditing and tax planning Diagnosing illnesses Commercial loan decisions Determining the cause of machine failure

What is the difference between a diagnostic and prescriptive task?

People In An Expert System

Domain Expert - the person who knows how to solve the problem without the aid of IT.

Knowledge Engineer - the person who works with domain experts to capture knowledge they possess. The knowledge engineer builds the expert system.

End User - the person who uses the expert system to solve a problem.

Components of an Expert System

User InterfaceInference Engine

Knowledge Base

Working Memory

User

facts

recommendations

Components Defined• KNOWLEDGE BASE - stores the domain expertise (e.g.,

a collection of If-Then rules).

• INFERENCE ENGINE - processes the domain expertise and your problem facts to reach a conclusion.

• WORKING MEMORY – short term memory of the expert system; contains all the facts (initial facts as well as new facts).

• USER INTERFACE – part of the expert system that you use to run a consultation.

Representing Expertise as a Collection of Rules

IF the light is green THENGo through the intersection

If the light is red THENSTOP

If the light is yellow AND there is time to go through intersection before the light turns red THEN

Go through the intersection

If the light is yellow AND there is not time to go through intersection before the light turns red THEN

STOP

A more complex example

IF 1. The infection that requires therapy is meningitis AND 2. The patient has evidence of serious skin or soft tissue infection AND 3. Organisms were not seen on the stain of the culture AND 4. The type of infection is bacterial

THEN There is evidence that the organism that might be causing the infection is

Staphylococcus coagpos (0.75) or Streptococcus (0.5)

Inference Engine

It is the part of the Expert System that processes the problem facts and searches for rules in the knowledge base to reach a final recommendation for a user. Two inferencing strategies :

Forward Chaining is a data-driven approach in which you start with the initial problem facts, and then try to draw conclusions from them using the rules of the knowledge base.

Backward Chaining is a goal-driven approach in which you start with some kind of expectation of what is to occur, or hypothesis, and then find rules that either support or contradict your hypothesis.

Illustrating Forward and Backward ChainingKnowledge BaseR1: IF A and C, THEN ER2: IF D and C, THEN FR3: IF B and E, THEN FR4: IF B, THEN CR5: IF F, THEN G

Two Problems:1. Forward Chain: Assume B and D2. Backward Chain: Prove or

disprove G, and assume A and B

Expert System Opportunities

Any activity where human experts are overburdened, undersupplied, or expensive are good candidates for ES.

• Expertise might be scarce in some organizations (can propagate the expertise through the use of an ES).

• An ES might also be used to enhance the role of an expert by providing the necessary assistance.

Benefits of Expert Systems

• Increased output and productivity• Reduced costs, including decreased personnel

required• Fewer errors• Better and more consistent decision-making• Knowledge transfer to remote locations• Formalization of organizational knowledge

Questions for thought What are some problems and

limitations of expert systems? Can expert systems solve all kinds

of problems?

Machine Learning

Field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959)

Example: Checkers playing program that sees tens of thousands of examples of board positions, and learn over time what the good positions are.

Learning from Data (see video)

Regression problems (predicting continuous-valued outcome) Predicting price of home from its size Predicting price of home based on multiple variables (size,

year built, location, condition of the building, etc)

Classification problems (predicting discrete-valued outcome) Determining malignancy of a tumor based on its size Determining malignancy of a tumor based on multiple

variables (size, age of patient, uniformity of tumor, etc.)

Some Examples of Machine Learning

A credit card company wants to predict whether a credit card transaction is fraudulent or not.

A company that sells ice cream wants to predict how much ice cream to produce over the summer months (June – August).

A software company wants to design an email spam filter to predict whether an email is spam or not.

A marketing researcher has customer data and wants to predict who among the customers are the most profitable.

Which of the above are classification problems and which are regression problems?

Housing price prediction.

Price ($) in 1000’s

Size in feet2

Regression: Predict continuous valued output (price)

Supervised Learning“right answers” given

Breast cancer (malignant, benign)

ClassificationDiscrete valued output (0 or 1)

Malignant?

1(Y)

0(N)

Tumor Size

Tumor Size

Tumor Size

Age

- Clump Thickness- Uniformity of Cell

Size- Uniformity of Cell

Shape…

A Neural Network

is an artificial intelligence system which is capable of learning to recognize patterns and relationships in the data it processes.

A neural network simulates the human ability to classify things based on the experience of seeing many examples.

A Neural Network can perform pattern recognition tasks like:

Detecting anomalies in human tissue that may signify disease

Reading handwriting Speech recognition Detecting abnormal patterns in

electrocardiographs

An Intelligent Agent

is an artificial intelligence system that can move around your computer or network performing repetitive tasks independently, adapting itself to your preferences.

An intelligent agent is like a travel agent in that it performs tasks that you stipulate.

Examples

Intelligent search engines Search engines that know who you

are, your preferences, where you are, who your friends are, etc.

Personal assistants Check and filter your e-mails Search the web and collect important

news items for you

Intelligent Agent Characteristics

Autonomy Adaptivity Sociability

Recap and Summary

Types of decisions Decision Support Systems OLAP (online analytical processing) Supporting groups with technology Expert Systems Machine Learning Intelligent Agents