artificial intelligence - khu.ac.krcvlab.khu.ac.kr/talk1.pdf · · 2014-05-26expert system (2)...
TRANSCRIPT
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: [email protected]
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!