Expert System in artificial intelligence

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CHAPTER 10
Knowledge-Based Decision
Support: Artificial Intelligence and
Expert Systems

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Knowledge-Based Decision
Support: Artificial
Intelligence and Expert
Systems
 Managerial Decision Makers are
Knowledge Workers
 Use Knowledge in Decision Making
 Accessibility to Knowledge Issue
 Knowledge-Based Decision Support:
Applied Artificial Intelligence
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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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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Basic Concepts Of Expert
Systems
 Expertise
 Transferring Experts
 Inferencing
 Rules
 Explanation Capability

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Expertise
 The extensive, task-specific knowledge acquired
from training, reading and experience
– Theories about the problem area
– Hard-and-fast rules and procedures
– Rules (heuristics)
– Global strategies
– Meta-knowledge (knowledge about knowledge)
– Facts
 Enables experts to be better and faster than
nonexperts

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Some Facts about
Expertise
 Expertise is usually associated with a high
degree of intelligence, but not always with the
smartest person
 Expertise is usually associated with a vast
quantity of knowledge
 Experts learn from past successes and
mistakes
 Expert knowledge is well-stored, organized and
retrievable quickly from an expert
 Experts have excellent recall

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Experts
 Degrees or levels of expertise
 Nonexperts outnumber experts
often by 100 to 1

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Human Expert Behaviors
 Recognize and formulate the problem
 Solve problems quickly and properly
 Explain the solution
 Learn from experience
 Restructure knowledge
 Break rules
 Determine relevance
 Degrade gracefully

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Transferring Expertise
 Objective of an expert system
– To transfer expertise from an expert to a
computer system and
– Then on to other humans (nonexperts)
 Activities
– Knowledge acquisition
– Knowledge representation
– Knowledge inferencing
– Knowledge transfer to the user
 Knowledge is stored in a knowledge base

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Two Knowledge Types
 Facts
 Procedures (usually rules)
Regarding the Problem Domain
•Deep knowledge
Knowledge base contains complex knowledge
•Self-knowledge
Able to examine own reasoning
Explain why conclusion reached

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Inferencing
 Reasoning (Thinking)
 The computer is programmed so
that it can make inferences
 Performed by the Inference Engine

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Rules
 IF-THEN-ELSE
 Explanation Capability
– By the justifier, or explanation
subsystem

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Explanation Capability
 Explain advice or
recommendations
– By the justifier, or explanation
subsystem

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Applications
 Finance
– Insurance evaluation, credit analysis, tax planning, financial
planning and reporting, performance evaluation
 Data processing
– Systems planning, equipment maintenance, vendor evaluation,
network management
 Marketing
– Customer-relationship management, market analysis, product
planning
 Human resources
– HR planning, performance evaluation, scheduling, pension
management, legal advising
 Manufacturing
– Production planning, quality management, product design, plant
site selection, equipment maintenance and repair

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Structure of
Expert Systems
 Development Environment
 Consultation (Runtime)
Environment

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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
 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
 It provides direction about how to use
system’s knowledge by developing the
agenda that organizes and controls
the steps taken to solve problems
whenever consultation takes place.

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User Interface
 Language processor for friendly,
problem-oriented communication
 menus and graphics
– Scrolling dialog interface: It is easiest to implement
and communicate with the user.
– Pop-up menus, windows, mice are more
advanced interfaces and powerful tools for
communicating with the user; they require graphics
support.

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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(how to attack on problem)
2. Agenda(Action execution)
3. Solution(candidate action)

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Explanation Subsystem
(Justifier)
 Traces responsibility and explains the ES
behavior by interactively answering
questions
-Why?
-How?
-What?
-(Where? When? Who?)

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Knowledge refinement system
 Knowledge Refining System
– Learning for improving performance
• Analyzes knowledge and use for learning and
improvements

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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
 Define relationship among facts
which are important in solving
problem.

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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)

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Other Participants
 System Builder
 Systems Analyst
 Tool Builder
 Vendors
 Support Staff
 Network Expert

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How Expert Systems Work
Major Activities of
ES Construction and Use
 Development
 Consultation
 Improvement

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ES Development
 Knowledge base development
 Knowledge separated into
– Declarative (factual) knowledge and
– Procedural knowledge
 Development (or Acquisition) of an
inference engine, blackboard, explanation
facility, or any other software
 Determine knowledge representations

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ES Shell
 Includes All Generic ES
Components
 But No Knowledge

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Consultation
 Deploy ES to Users (Typically
Novices)
 ES Must be Very Easy to Use
 ES Improvement
– By Rapid Prototyping

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Improvement
 ES Improvement
– By Rapid Prototyping

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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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by Expert Systems
 Interpretation systems
– Surveillance, image analysis, signal interpretation
 Prediction systems
– Weather forecasting, traffic predictions, demographics
 Diagnostic systems
– Medical, mechanical, electronic, software diagnosis
 Design systems
– Circuit layouts, building design, plant layout
 Planning systems
– Project management, routing, communications, financial
plans

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by Expert Systems
 Monitoring systems
– Air traffic control, fiscal management tasks
 Debugging systems
– Mechanical and software
 Repair systems
– Incorporate debugging, planning, and execution capabilities
 Instruction systems
– Identify weaknesses in knowledge and appropriate remedies
 Control systems
– Life support, artificial environment

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Expert Systems Benefits
 Increased Output and Productivity
 Decreased Decision Making Time
 Increased Process(es) 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
 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 Information system

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Lead to
 Improved decision making
 Improved products and customer
service
 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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Types of Expert Systems
 Rule-based Systems
– Knowledge represented by series of rules
 Frame-based Systems
– Knowledge represented by frames
 Hybrid Systems
– Several approaches are combined, usually rules and frames
 Model-based Systems
– Models simulate structure and functions of systems
 Off-the-shelf Systems
– Ready made packages for general use
 Custom-made Systems
– Meet specific need
 Real-time Systems
– Strict limits set on system response times

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Expert Systems and the
Web/Internet/Intranets
1. Use of ES on the Net
2. Support ES (and other AI methods)

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Using ES on the Web
 Provide knowledge and advice
 Help desks
 Knowledge acquisition
 Spread of multimedia-based expert
systems (Intelimedia systems)
 Support ES and other AI technologies
provided to the Internet/Intranet

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