Title: Welcome to IS 335 Expert Systems and Decision Support Systems
1Welcome to IS 335Expert Systems and Decision
Support Systems
- Dr.Khalid A. Eldrandaly,PhD,GISP
- Professor of IS
2LECTURE Three
3What 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.
4AI 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.
5AI Objectives
- Make machines smarter
- Understand what intelligence is
- Make machines more useful
6Signs 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
Dr.Khalid Eldrandaly
7Turing 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
Dr.Khalid Eldrandaly
8Artificial Intelligence versus Natural
Intelligence
Dr. Khalid Eldrandaly
9AI 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
Dr. Khalid Eldrandaly
10Natural 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
Dr. Khalid Eldrandaly
11AI 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
Dr. Khalid Eldrandaly
12The AI Field
- Many Different Sciences Technologies
- Linguistics
- Psychology
- Philosophy
- Computer Science
- Electrical Engineering
- Hardware and Software
- Etc.
Dr. Khalid Eldrandaly
13Major AI Areas
- Expert Systems
- Natural Language Processing
- Speech Understanding
- Robotics and Computer Vision
- Smart Computing
- Etc.
Dr. Khalid Eldrandaly
14EXPERT 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.
15Expert 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
Dr. Khalid Eldrandaly
16Expert 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
Dr. Khalid Eldrandaly
17Procedural Systems
- use previously defined procedures
- use numerical processing
- use linear processing
- developed and maintained by programmers
- structured designed
- information and control integrated
- cant explain its reasoning
18Expert 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
19Knowledge 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
20Knowledge 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.
21Knowledge Engineering Activities
Knowledge Engineer
Expert System
Domain Expert
22Expert Systems Architecture
- The term architecture refers to the science and
method of design that determine the structure of
the expert system.
23Three Major ES Components
- Knowledge Base
- Inference Engine
- User Interface
Dr. Khalid Eldrandaly
24Three Major ES Components
User Interface
Knowledge Base
Dr. Khalid Eldrandaly
25All ES Components
- Knowledge Acquisition Subsystem
- Knowledge Base
- Inference Engine
- User Interface
- Blackboard (Workplace)
- Explanation Subsystem (Justifier)
- Knowledge Refining System
- User
- Most ES do not have a Knowledge Refinement
Component
Dr. Khalid Eldrandaly
26Knowledge Acquisition Subsystem
- Knowledge acquisition is the accumulation,
transfer and transformation of problem-solving
expertise from experts and/or documented
knowledge sources to a computer program for
constructing or expanding the knowledge base - Requires a knowledge engineer
Dr. Khalid Eldrandaly
27Knowledge 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
Dr. Khalid Eldrandaly
28Inference Engine
- The brain of the ES
- The control structure (rule interpreter)
- Provides methodology for reasoning
Dr. Khalid Eldrandaly
29User Interface
- Language processor for friendly, problem-oriented
communication - NLP, or menus and graphics
Dr. Khalid Eldrandaly
30Blackboard (Workplace)
- Area of working memory to
- Describe the current problem
- Record Intermediate results
- Records Intermediate Hypotheses and Decisions
- 1. Plan
- 2. Agenda
- 3. Solution
Dr. Khalid Eldrandaly
31Explanation 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
Dr. Khalid Eldrandaly
32The Human Element in Expert Systems
- Expert
- Knowledge Engineer
- User
- Others
Dr. Khalid Eldrandaly
33The Expert
- Has the special knowledge, judgment, experience
and methods to give advice and solve problems - Provides knowledge about task performance
Dr. Khalid Eldrandaly
34The 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
Dr. Khalid Eldrandaly
35The 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
Dr. Khalid Eldrandaly
36Other Participants
- System Builder
- Systems Analyst
- Tool Builder
- Vendors
- Support Staff
- Network Expert
Dr. Khalid Eldrandaly
37Expert Systems Building Tools
- Languages
- Conventional languages such as C
- AI languages such as PROLOG
- Shells such as EXSYS
- Knowledge Engineering Environments such as VRS
38Problem 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
Dr. Khalid Eldrandaly
39Expert 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
Dr. Khalid Eldrandaly
40- 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
Dr. Khalid Eldrandaly
41Lead to
- Improved decision making
- Improved products and customer service
- Sustainable strategic advantage
- May enhance organizations image
Dr. Khalid Eldrandaly
42Problems and Limitations of Expert Systems
- Knowledge is not always readily available
- Expertise can be hard to extract from humans
- Each experts 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
Dr. Khalid Eldrandaly
43- 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
Dr. Khalid Eldrandaly
44Limitations 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
45- See you next Wednesday inshaa Allah to
- discuss the following important topic
- Knowledge Engineering
- Good Luck