Transcript Artificial intelligence Powerpoint presentation

Chapter 12:
Artificial Intelligence and
Expert Systems
Artificial Intelligence (AI)
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Artificial intelligence (AI) ) ‫(الذكاء االصطناعي‬
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AI has many definitions…
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A subfield of computer science, concerned with
symbolic reasoning ( ‫ ) المنطق الرمزي‬and problem
solving
Behavior by a machine that, if performed by a
human being, would be considered intelligent
“…study of how to make computers do things at
which, at the moment, people are better
Theory of how the human mind works
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AI Objectives
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Make machines smarter (primary goal)
Understand what intelligence is
Make machines more intelligent and useful
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Signs of intelligence…
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Learn or understand from experience
Make sense out of ambiguous )‫ )غامض‬situations
Respond quickly to new situations
Use reasoning to solve problems
Apply knowledge to manipulate the environment
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Symbolic Processing
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AI …
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represents knowledge as a set of symbols, and
uses these symbols to represent problems, and
apply various strategies and rules to manipulate
symbols to solve problems
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Artificial vs. Natural Intelligence
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Advantages of AI
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Advantages of Biological Natural Intelligence
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More permanent
Ease of duplication )‫)االزدواجية‬and dissemination(‫(نشر‬
Less expensive
Consistent and thorough ) ‫(متسقة و شاملة‬
Can be documented
Can execute certain tasks much faster
Can perform certain tasks better than many people
Is truly creative
Can use sensory )‫ شعوري‬،‫(حسي‬input directly and
creatively
Can apply experience in different situations
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The AI Field
AI is many different sciences and technologies
It is a collection of concepts and ideas
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Linguistics
Psychology
Philosophy
Computer Science
Electrical Engineering
Mechanics
Hydraulics ‫(علم السوائل‬
) ‫المتحركة‬
Physics
Optics ) ‫(علم البصريات‬
Management and
Organization Theory
Chemistry
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Chemistry
Physics
Statistics
Mathematics
Management Science
Management Information Systems
Computer hardware and software
Commercial, Government and
Military Organizations ‫(المنظمات‬
) ‫العسكرية‬
…
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The AI Field…
Intelligent tutoring
AI provides the
scientific
foundation for
many commercial
technologies
Natural Language Processing
Speech Understanding
Voice Recognition
Automatic Programming
Machine Learning
Computer Vision
Applications
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Intelligent Agents
Autonomous Robots
Neural Networks
Genetic Algorithms
Game Playing
Expert Systems
Fuzzy Logic
The AI
Tree
Mathematics
Computer Science
Philosophy
Disciplines
Human Behavior
Neurology
Engineering
Logic
Robotics
Information Systems
Sociology
Statistics
Psychology
Pattern Recognition
Human Cognition
Linguistics
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Management Science
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Biology
AI Areas
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Major…
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Additional…
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Expert Systems
Natural Language Processing
Speech Understanding
Robotics and Sensory Systems
Computer Vision and Scene Recognition
Intelligent Computer-Aided Instruction
Automated Programming ) ‫(برمجة آلية‬
Neural Computing Game Playing
Game Playing, Language Translation
Fuzzy Logic ) ‫(المنطق الضيابي‬, Genetic Algorithms ‫(الخوازميات‬
) ‫الجينية‬
Intelligent Software Agents
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AI is often transparent in many
commercial products
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Anti-lock Braking Systems (ABS)
Automatic Transmissions
Video Camcorders
Appliances
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Help Desk Software
Subway Control…
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Washers, Toasters, Stoves
Sendai Subway 1000 series
(http://en.wikipedia.org/wiki/Sendai_Subway_1000_series)
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Expert Systems (ES)
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ES is a computer program that attempts to
imitate expert’s reasoning processes and
knowledge in solving specific problems
Most Popular Applied AI Technology
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Works best with narrow ) ‫ ( ضيق‬problem
areas/tasks
Expert systems do not replace experts, but
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Enhance Productivity
Augment ) ‫ ( زيادة‬Work Forces
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Make their knowledge and experience more widely
available, and thus
Permit non-experts to work better
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Important Concepts in ES
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Expert )‫(خبير‬
A human being who has developed a high level of
proficiency ) ‫ (كفاءة‬in making judgments in a specific
domain
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Expertise ) ‫(الخبرة‬
The set of capabilities that underlines the
performance of human experts, including
extensive domain knowledge,
 heuristic ) ‫( الكشف عن مجريات األمور‬rules that simplify and
improve approaches to problem solving,
 meta-knowledge )‫( الفوقية‬and meta-cognition) ‫( اإلدراك‬, and
 compiled forms of behavior that afford great economy in
a skilled
performance
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The Human Element in ES
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Expert
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Knowledge Engineer
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Helps the expert(s) structure the problem area by
interpreting )‫( تفسير‬and integrating human answers
to questions, drawing analogies, posing counter
examples ) ‫( طرح أمثلة مضادة‬, and enlightening
conceptual difficulties ) ‫( إنارة المافاهيم الصعبة‬
User
Others
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Has the special knowledge, judgment, experience
and methods to give advice and solve problems
System Analyst, Builder, Support Staff, …
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Knowledge and Inference Rules (
) ‫قواعد االستدالل‬
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Two types of rules are common in AI:
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Knowledge rules (declarative rules) ) ‫(قواعد تعريفية‬,
state all the facts and relationships about a problem
Inference rules (procedural rules) ) ‫(قواعد إجرائية‬,
advise on how to solve a problem, given that certain
facts are known
Inference rules contain rules about rules (metarules)
Knowledge rules are stored in the knowledge base
Inference rules become part of the inference engine
Example:
IF needed data is not known THEN ask the user
 IF more than one rule applies THEN fire the one with the
priority
first as Prentice Hall
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Knowledge rules and Inference rules
Inferencing )‫( استدالل‬with Uncertainty
Combining Certainty Factors
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Combining Several Certainty Factors in One Rule where
parts are combined using AND and OR logical operators
AND
IF inflation is high, CF = 50 percent, (A), AND
unemployment rate is above 7, CF = 70 percent, (B), AND
bond prices decline, CF = 100 percent, (C)
THEN stock prices decline
CF(A, B, and C) = Minimum[CF(A), CF(B), CF(C)]
=>
 The CF for “stock prices to decline” = 50 percent
 The chain is as strong as its weakest link
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Inferencing with Uncertainty
Combining Certainty Factors
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OR
IF inflation is low, CF = 70 percent, (A), OR
bond prices are high, CF = 85 percent, (B)
THEN stock prices will be high
CF(A, B) = Maximum[CF(A), CF(B)]
=>
 The CF for “stock prices to be high” = 85 percent
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Notice that in OR only one IF premise needs to be
true
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Inferencing with Uncertainty
Combining Certainty Factors
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Combining two or more rules
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Example:
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R1:
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R2:
Inflation rate = 4 percent and the unemployment
level = 6.5 percent
Combined Effect
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IF the inflation rate is less than 5 percent,
THEN stock market prices go up (CF = 0.7)
IF unemployment level is less than 7 percent,
THEN stock market prices go up (CF = 0.6)
CF(R1,R2) = CF(R1) + CF(R2)[1 - CF(R1)]; or
CF(R1,R2) = CF(R1) + CF(R2) - CF(R1)  CF(R2)
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Inferencing with Uncertainty
Combining Certainty Factors
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Example continued…
Given CF(R1) = 0.7 AND CF(R2) = 0.6, then:
CF(R1,R2) = 0.7 + 0.6(1 - 0.7) = 0.7 + 0.6(0.3) = 0.88
Expert System tells us that there is an 88 percent chance that
stock prices will increase
For a third rule to be added
CF(R1,R2,R3) = CF(R1,R2) + CF(R3) [1 - CF(R1,R2)]
R3: IF bond price increases THEN stock prices go up (CF = 0.85)
Assuming all rules are true in their IF part, the chance that stock
prices will go up is
CF(R1,R2,R3) = 0.88 + 0.85 (1 - 0.88) = 0.982
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Inferencing with Uncertainty
Certainty Factors - Example
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Rules
R1: IF blood test result is yes
THEN the disease is malaria (CF 0.8)
R2: IF living in malaria zone
THEN the disease is malaria (CF 0.5)
R3: IF bit by a flying bug
THEN the disease is malaria (CF 0.3)
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Questions
What is the CF for having malaria (as its calculated by ES), if
1. The first two rules are considered to be true ?
2. All three rules are considered to be true?
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Inferencing with Uncertainty
Certainty Factors - Example
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Questions
What is the CF for having malaria (as its calculated by ES), if
1. The first two rules are considered to be true ?
2. All three rules are considered to be true?
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Answer 1
1. CF(R1, R2)= CF(R1) + CF(R2) * (1 – CF(R1)
= 0.8 + 0.5 * (1 - 0.8) = 0.8 – 0.1 = 0.9
2. CF(R1, R2, R3) = CF(R1, R2) + CF(R3) * (1 - CF(R1, R2))
= 0.9 + 0.3 * (1 - 0.9) = 0.9 – 0.03 = 0.93
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Answer 2
1. CF(R1, R2)= CF(R1) + CF(R2) – (CF(R1) * CF(R2))
= 0.8 + 0.5 – (0.8 * 0.5) = 1.3 – 0.4 = 0.9
2. CF(R1, R2, R3) = CF(R1, R2) + CF(R3) – (CF(R1, R2) * CF(R3))
= 0.9 + 0.3 – (0.9 * 0.3) = 1.2 – 0.27 = 0.93
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How ES Work:
Inference Mechanisms
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Development process of ES
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A typical process for developing ES includes:
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Knowledge acquisition ) ‫(اكتساب المعرفة‬
Knowledge representation
Selection of development tools
System prototyping
Evaluation
Improvement /Maintenance
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Problem Areas Addressed by ES
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Interpretation systems
Prediction systems
Diagnostic systems ) ‫(نظام التشخيص‬
Repair systems
Design systems
Planning systems
Monitoring systems
Debugging systems) ‫( نظام التصحيح‬
Instruction systems
Control systems, …
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ES Benefits
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Capture Scarce Expertise
Increased Productivity and Quality
Decreased Decision Making Time
Reduced Downtime via Diagnosis ) ‫(تشخيص‬
Easier Equipment Operation
Elimination of Expensive Equipment
Ability to Solve Complex Problems
Knowledge Transfer to Remote Locations ‫(المناطق‬
) ‫النائية‬
Integration of Several Experts' Opinions
Can Work with Uncertain Information
… more …
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Problems and Limitations of ES
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Knowledge is not always readily available
Expertise can be hard to extract from humans
 Fear of sharing expertise
 Conflicts arise in dealing with multiple experts
ES work well only in a narrow domain of knowledge
Experts’ vocabulary often highly technical
Knowledge engineers are rare ) ‫(نادر‬and expensive
Lack of trust by end-users
ES sometimes produce incorrect recommendations
… more …
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ES Success Factors
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Most Critical Factors
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Having a Champion in Management
User Involvement and Training
Justification of the Importance of the Problem
Good Project Management
The level of knowledge must be sufficiently high
There must be (at least) one cooperative expert
The problem must be mostly qualitative
The problem must be sufficiently narrow in scope
The ES shell must be high quality, with friendly user
interface, and naturally store and manipulate the
knowledge
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