Otabek Khujaev

Professor, Computer Engineering Dept. Urgench branch Tashkent university of information

technologies

[CSE10100] Introduction to Computer

Engineering (컴퓨터공학 개론)

Artificial Intelligence

Personal Information

Name: Otabek Surname: Khujaev

Nationality: Uzbek Marital Status: Married

Home Phone No.: +998623944528 Mobile Phone No.: +998919133050

Date of Birth: 27.10.1986 Passport No.: AA 3741402

E-mail & Messenger: otabek.hujaev@gmail.com

Present Address: “Kirk-yap” village, Khanka district, Khorezm region, Uzbekistan.

Education History

University

Period

Major

Degree

Graduation

Year

Thesis

Tashkent University of

Information Technologies

(Uzbekistan)

01.01.2011 to

present

Mathematical tools

and software for

computers,

complexes, systems

and networks

Ph.D. 2015

Models and

algorithms

for for data

mining in

environment

semi-

structured

databases.

Tashkent University of

Information Technologies

(Uzbekistan)

01.09.2007-

06.08.2009

Mathematical tools

and software for

computers,

complexes, systems

and networks

master 2009

Creating

software

central

database of

filling illness

and statistical

analyze it.

Tashkent University of

Information Technologies

(Uzbekistan)

01.09.2003-

01.07.2007

Information

technologies bachelor 2007

Creating

software

working with

farmers in oil

factories

About Uzbekistan & our university

Our university is main university on information

technologies in Uzbekistan

www.tuit.uz

Five branches in regions

Overview of Artificial Intelligence

• Artificial intelligence (AI)

– Computers with the ability to mimic or duplicate the functions of the human brain

• Artificial intelligence systems

– The people, procedures, hardware, software, data, and knowledge needed to develop computer systems and machines that demonstrate the characteristics of intelligence

Overview of Artificial Intelligence

• Intelligent behaviour – Learn from experience – Apply knowledge acquired from experience – Handle complex situations – Solve problems when important information is missing – Determine what is important – React quickly and correctly to a new situation – Understand visual images – Process and manipulate symbols – Be creative and imaginative – Use heuristics

Artificial Intellegence

Artificial intellegence

The field of artificial intelligence has many branches. Today we explore

the following issues in the world of AI:

■ Knowledge representation—the techniques used to represent knowl-

edge so that a computer system can apply it to intelligent problem

solving

■ Expert systems—computer systems that embody the knowledge of

human experts

■ Neural networks—computer systems that mimic the processing of

the human brain

Knowledge representation(Semantic networks)

Semantic Web

The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C). The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a "web of data". The Semantic Web stack builds on the W3C's Resource Description Framework (RDF).

An example of a tag that would be used in a non-semantic web page:

<item>blog</item>

Encoding similar information in a semantic web page might look like this:

<item rdf:about="http://example.org/semantic-web/">Semantic Web</item>

Protege Protégé is a free, open source ontology editor and a knowledge acquisition system..

Protégé recently has over 200,000 registered users. Protégé is being developed

at Stanford University in collaboration with the University of Manchester and is made

available under the Mozilla Public License

Protege

Protege

Protege

Overview of Expert Systems

• Can… – Explain their reasoning or suggested decisions

– Display intelligent behavior

– Draw conclusions from complex relationships

– Provide portable knowledge

• Expert system shell – A collection of software packages and tools

used to develop expert systems

Limitations of Expert Systems

• Not widely used or tested

• Limited to relatively narrow problems

• Cannot readily deal with “mixed” knowledge

• Possibility of error

• Cannot refine own knowledge base

• Difficult to maintain

• May have high development costs

• Raise legal and ethical concerns

Capabilities of Expert Systems

Strategic goal setting

Decision making

Planning

Design

Quality control and monitoring

Diagnosis

Explore impact of strategic goals

Impact of plans on resources

Integrate general design principles and manufacturing limitations

Provide advise on decisions

Monitor quality and assist in finding solutions

Look for causes and suggest solutions

Components of an

Expert System (1) • Knowledge base

– Stores all relevant information, data, rules, cases, and relationships used by the expert system

• Inference engine – Seeks information and relationships from the knowledge

base and provides answers, predictions, and suggestions in the way a human expert would

• Rule – A conditional statement that links given conditions to

actions or outcomes

Components of an Expert System (2)

• Fuzzy logic – A specialty research area in computer science that allows

shades of gray and does not require everything to be simply yes/no, or true/false

• Backward chaining – A method of reasoning that starts with conclusions and

works backward to the supporting facts

• Forward chaining – A method of reasoning that starts with the facts and works

forward to the conclusions

Inference engine

Explanation facility

Knowledge base

acquisition facility

User interface

Knowledge base

Experts User

Rules for a Credit Application

Mortgage application for a loan for $100,000 to $200,000

If there are no previous credits problems, and

If month net income is greater than 4x monthly loan payment, and

If down payment is 15% of total value of property, and

If net income of borrower is > $25,000, and

If employment is > 3 years at same company

Then accept the applications

Else check other credit rules

Explanation Facility

• Explanation facility

– A part of the expert system that allows a user or decision maker to understand how the expert system arrived at certain conclusions or results

Knowledge Acquisition Facility

– Knowledge acquisition facility

• Provides a convenient and efficient means of capturing and storing all components of the knowledge base

Knowledge base

Knowledge acquisition

facility

Joe Expert

Determining requirements

Identifying experts

Construct expert system components

Implementing results

Maintaining and reviewing system

Expert Systems Development

Domain • The area of knowledge

addressed by the expert system.

Participants in Expert Systems

Development and Use • Domain expert

– The individual or group whose expertise and knowledge is captured for use in an expert system

• Knowledge user – The individual or group who uses and benefits from the

expert system

• Knowledge engineer – Someone trained or experienced in the design,

development, implementation, and maintenance of an expert system

Expert system

Domain expert

Knowledge engineer

Knowledge user

Applications of expert systems Category Problem Addressed Examples

Interpretation Inferring situation descriptions

from sensor data

Hearsay (Speech Recognition),

PROSPECTOR

Prediction Inferring likely consequences of

given situations Pretirm Birth Risk Assessment

Diagnosis Inferring system malfunctions from

observables

CADUCEUS, MYCIN, PUFF,

Mistral

Design Configuring objects under

constraints

Dendral, Mortgage Loan Advisor,

R1 (Dec Vax Configuration)

Planning Designing actions Mission Planning for Autonomous

Underwater Vehicle

Monitoring Comparing observations to plan

vulnerabilities REACTOR

Debugging Providing incremental solutions for

complex problems SAINT, MATHLAB, MACSYMA

Repair Executing a plan to administer a

prescribed remedy Toxic Spill Crisis Management

Instruction Diagnosing, assessing, and

repairing student behavior

SMH.PAL, Intelligent Clinical

Training, STEAMER

Control Interpreting, predicting, repairing,

and monitoring system behaviors

Real Time Process Control, Space

Shuttle Mission Control

Clinical decision support system

Clinical decision support system (CDSS) is an interactive Expert system Computer Software, which is designed to assist physicians and other health professionals with decision making tasks, such as determining diagnosis of patient data. For example:

SimulConsult CDSS-SimulConsult's medical decision support software allows doctors and other medical professionals to combine clinical and laboratory findings and get a "simultaneous consult" about diagnosis. The software suggests diagnoses and also identifies other findings that will be most useful in reaching a diagnosis.

SimulConsult CDSS

Neural Networks

• Question #1: which lamp is turning on of traffic light?

This question is very easy, red

This is more complex task for

me

Neural Networks

• Question#2: Calculate this expression?

This question is more complex for

me This is very easy

for me, 1307674368000

15!

Neural Networks

Why Question#1 is difficult for computer, easy

for schoolboy and Question#2 is easy for

computer and difficult for schoolboy?

Therefor scientists research working principles

of human brain and try to modeling

Biological Neural Networks

Aftificial Neural Network

Neural Network models

• Feedforward NN Reccurent NN

Training Process

• Training process is searching most suitable weight matrix

Sharky Neural Network

AI Tools AI has developed a large number of tools to solve the

most difficult problems in computer science. A few of

the most general of these methods are discussed below.

• Search and optimization

• Logic

• Probabilistic methods for uncertain reasoning

• Classifiers and statistical learning methods

• Neural networks

• Control theory

• Languages

Data analitcs (KNIME)

Classification Iris flowers with decision tree method

Training data

Test data

Classification result

Thank you for attention!