Transcript Expert Systems

Welcome to IS 335
Expert Systems and Decision
Support Systems
Dr.Khalid A. Eldrandaly,PhD,GISP
Professor of IS
Dr.Khalid Eldrandaly
LECTURE Three
Expert Systems Overview
Dr.Khalid Eldrandaly
What is intelligence ?
There is no unique definition of intelligence.
Webster's dictionary defines intelligence as, " the ability to
understand new or trying situations ".
The more commonly accepted definition is " the ability to
perceive, understand and learn about new situations ".
The human brain is equipped with such an enormous potential to
perceive, understand and learn. If this ability can be duplicated in
a computer system, the computer should be classified as being
intelligence according to the definition of intelligence .
As the human intelligence is captured by an external system;
hence the name artificial intelligence.
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AI Concepts and Definitions
The origins of AI can be traced back to 1950s. In 1956,
at a Dartmouth Conference, John McCarthy coined the
term “artificial intelligence (AI).”
AI aims to understand human cognitive processes and
modeling them on the computer so that the computer
can solve the process the same way the human would do.
AI can be defined as the field of computer science
concerned with designing intelligent computer systems.
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AI Objectives
Make machines smarter
Understand what intelligence is
Make machines more useful
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Signs of Intelligence
Learn or understand from experience
Make sense out of ambiguous or
contradictory messages
Respond quickly and successfully to new
situations
Use reasoning to solve problems
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Turing Test for Intelligence
A computer can be considered to be smart
only when a human interviewer,
“conversing” with both an unseen human
being and an unseen computer, can not
determine which is which
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Artificial Intelligence
versus
Natural Intelligence
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AI Advantages Over Natural Intelligence
More permanent
Ease of duplication and dissemination
Less expensive
Consistent and thorough
Can be documented
Can execute certain tasks much faster than a
human
Can perform certain tasks better than many or
even most people
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Natural Intelligence Advantages over AI
Natural intelligence is creative
People use sensory experience directly
Can use a wide context of experience in
different situations
AI - Very Narrow Focus
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AI Methods are Valuable
Models of how we think
Methods to apply our intelligence
Can make computers easier to use
Can make more knowledge available
Simulate parts of the human mind
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The AI Field
Many Different Sciences & Technologies
Linguistics
Psychology
Philosophy
Computer Science
Electrical Engineering
Hardware and Software
Etc.
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Major AI Areas
Expert Systems
Natural Language Processing
Speech Understanding
Robotics and Computer Vision
Smart Computing
Etc.
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EXPERT SYSTEMS
In 1970s AI scientists laid a conceptual
breakthrough in AI field, which can be simply
stated “ to make a program intelligent, provide it
with lots of high-quality , specific knowledge
about some problem area.”
Expert systems(ES) can be defined as:
 A sophisticated computer program that
manipulate knowledge to solve problems
efficiently and effectively in a narrow area.
 A computer program designed to model the
problem-solving ability of a human expert.
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Expert Systems
Attempt to Imitate Expert Reasoning
Processes and Knowledge in Solving
Specific Problems
Most Popular Applied AI Technology
Enhance Productivity
Augment Work Forces
Narrow Problem-Solving Areas or Tasks
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Expert Systems
Provide Direct Application of Expertise
Expert Systems Do Not Replace Experts, But
They
Make their Knowledge and Experience More
Widely Available
Permit Nonexperts to Work Better
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Procedural Systems
use previously defined procedures
use numerical processing
use linear processing
developed and maintained by programmers
structured designed
information and control integrated
can’t explain its reasoning
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Expert Systems
Use heuristics to solve problems
use formal reasoning
use parallel and interactive processing
developed and maintained by knowledge
engineers
interactive and cyclic design
knowledge and control separated
can explain its reasoning
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Knowledge Engineering
Knowledge engineering is the art of
bringing the principles and tools of
AI research to bear on difficult
applications
problem
requiring
experts knowledge for their solutions
knowledge engineering is the science
of building expert systems
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Knowledge Engineering Activities
Knowledge acquisition : collection of
knowledge from the domain expert.
Knowledge representation : representing
the knowledge collected, in some formal
scheme for implementation by computer.
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Knowledge Engineering Activities
Domain Expert
Knowledge
Engineer
Expert System
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Expert Systems Architecture
The term architecture refers to the
science and method of design that
determine the structure of the
expert system.
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Three Major ES Components
Knowledge Base
Inference Engine
User Interface
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Three Major ES Components
User Interface
Inference
Engine
Knowledge
Base
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All ES Components
Knowledge Acquisition Subsystem
Knowledge Base
Inference Engine
User Interface
Blackboard (Workplace)
Explanation Subsystem (Justifier)
Knowledge Refining System
User
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Most ES do not have a Knowledge Refinement Component
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Knowledge Acquisition Subsystem
Knowledge acquisition is the accumulation,
transfer and transformation of problemsolving expertise from experts and/or
documented knowledge sources to a computer
program for constructing or expanding the
knowledge base
Requires a knowledge engineer
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Knowledge Base
The knowledge base contains the knowledge necessary
for understanding, formulating, and solving problems
Two Basic Knowledge Base Elements
Facts
Special heuristics, or rules that direct the use of
knowledge
Knowledge is the primary raw material of ES
Incorporated knowledge representation
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Inference Engine
The brain of the ES
The control structure (rule interpreter)
Provides methodology for reasoning
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User Interface
Language processor for friendly, problemoriented communication
NLP, or menus and graphics
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Blackboard (Workplace)
Area of working memory to
Describe the current problem
Record Intermediate results
Records Intermediate Hypotheses and
Decisions
1. Plan
2. Agenda
3. Solution
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Explanation Subsystem (Justifier)
Traces responsibility and explains the ES
behavior by interactively answering
questions
-Why?
-How?
-What?
-(Where? When? Who?)
Knowledge Refining System
Learning for improving performance
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The Human Element in Expert Systems
Expert
Knowledge Engineer
User
Others
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The Expert
Has the special knowledge, judgment,
experience and methods to give advice
and solve problems
Provides knowledge about task
performance
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The Knowledge Engineer
Helps the expert(s) structure the problem
area by interpreting and integrating human
answers to questions, drawing analogies,
posing counterexamples, and bringing to
light conceptual difficulties
Usually also the System Builder
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The User
Possible Classes of Users
A non-expert client seeking direct advice (ES
acts as a Consultant or Advisor)
A student who wants to learn (Instructor)
An ES builder improving or increasing the
knowledge base (Partner)
An expert (Colleague or Assistant)
The Expert and the Knowledge Engineer Should
Anticipate Users' Needs and Limitations When
Designing ES
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Other Participants
System Builder
Systems Analyst
Tool Builder
Vendors
Support Staff
Network Expert
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Expert Systems Building Tools
Languages :
1. Conventional languages such as C
2. AI languages such as PROLOG
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Shells such as EXSYS
Knowledge Engineering Environments
such as VRS
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Problem Areas Addressed by Expert Systems
Interpretation systems
Prediction systems
Diagnostic systems
Design systems
Planning systems
Monitoring systems
Debugging systems
Repair systems
Instruction systems
Control systems
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Expert Systems Benefits
Increased Output and Productivity
Decreased Decision Making Time
Increased Processes and Product Quality
Reduced Downtime
Capture Scarce Expertise
Flexibility
Easier Equipment Operation
Elimination of Expensive Equipment
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Operation in Hazardous Environments
Accessibility to Knowledge and Help Desks
Integration of Several Experts' Opinions
Can Work with Incomplete or Uncertain Information
Provide Training
Enhancement of Problem Solving and Decision Making
Improved Decision Making Processes
Improved Decision Quality
Ability to Solve Complex Problems
Knowledge Transfer to Remote Locations
Enhancement of Other MIS
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Lead to
Improved decision making
Improved products and customer service
Sustainable strategic advantage
May enhance organization’s image
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Problems and Limitations of Expert Systems
Knowledge is not always readily available
Expertise can be hard to extract from humans
Each expert’s approach may be different, yet
correct
Hard, even for a highly skilled expert, to work under
time pressure
Expert system users have natural cognitive limits
ES work well only in a narrow domain of knowledge
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Most experts have no independent means to validate
their conclusions
Experts’ vocabulary often limited and highly technical
Knowledge engineers are rare and expensive
Lack of trust by end-users
Knowledge transfer subject to a host of perceptual and
judgmental biases
ES may not be able to arrive at valid conclusions
ES sometimes produce incorrect recommendations
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Limitations of Expert Systems
Expert Systems are not good at:
1- representing temporal knowledge
2- representing spatial knowledge
3- performing commonsense reasoning
4- handling inconsistent knowledge
5- recognizing the limits of their ability
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See you next Wednesday inshaa Allah to
discuss the following important topic:
Knowledge Engineering
Good Luck
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