Welcome to IS 335 Expert Systems and Decision Support Systems

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Welcome to IS 335Expert Systems and Decision
Support Systems

• Dr.Khalid A. Eldrandaly,PhD,GISP
• Professor of IS

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LECTURE Three

• Expert Systems Overview

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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
• cant 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
Knowledge Engineer
Expert System
Domain Expert
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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
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
• 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 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

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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, problem-oriented
  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
• Conventional languages such as C
• AI languages such as PROLOG
• 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 organizations 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 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

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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