Transcript Chapter 7 User Interface and Decision Visualization Applications

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MNGT 3140 – AI/ES
F. Nelson Ford, Ph.D.
Coordinator, MIS Programs
Department of Management
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Knowledge-based
Decision Support and
Artificial Intelligence
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Managerial Decision Makers are
Knowledge Workers
They Use Knowledge in Decision Making
Issue: Accessibility to Knowledge
Knowledge-Based Decision Support
Through Applied Artificial Intelligence
Tools
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
AI Concepts and Definitions
AI Involves Studying Human
Thought Processes (to Understand
What Intelligence Is) and
Representing Thought Processes
on Machines
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Artificial Intelligence
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Artificial intelligence is behavior by
a machine that, if performed by a
human being, would be called
intelligent (well-publicized)
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Artificial intelligence Deals Primarily
with Symbolic, Nonalgorithmic
Methods of Problem Solving
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Signs of Intelligence
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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
(Continued on next page)
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Signs of Intelligence
(cont’d)
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Deal with perplexing situations
Understand and Infer in ordinary,
rational ways
Apply knowledge to manipulate the
environment
Think and reason
Recognize the relative importance of
different elements in a situation
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Heuristic Methods for
Processing Information
Reasoning - Inferencing from Facts
and Rules using heuristics or other
search approaches
Pattern Matching
Attempt to describe objects, events,
or processes in terms of their
qualitative features and logical and
computational relationships
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Commercial Advantages of
AI Over Natural Intelligence
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AI is more permanent
AI offers ease of duplication and
dissemination
AI can be less expensive
AI is consistent and thorough
AI can be documented
AI can execute certain tasks much faster
than a human can
AI can perform certain tasks better than
many or even most people
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Natural Intelligence
Advantages over AI
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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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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The Artificial Intelligence
Field
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Involves Many Different Sciences
and Technologies
– Linguistics
– Psychology
– Philosophy
– Computer Science
– Electrical Engineering
– Hardware and Software
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Major AI Areas
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Expert Systems
Natural Language Processing
Speech Understanding
Fuzzy Logic
Robotics and Sensory Systems
Computer Vision and Scene
Recognition
Intelligent Computer-Aided Instruction
Machine Learning (Neural Computing)
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Intelligent Agents
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Fundamentals of Expert
Systems
CATS-1 at General Electric
The Problem:
General Electric's (GE)
Top Locomotive Field Service
Engineer was Nearing
Retirement
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Introduction
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Expert System: from the term
knowledge-based expert system
An Expert System is a system that
employs human knowledge
captured in a computer to solve
problems that ordinarily require
human expertise
ES imitate the expert’s reasoning
processes to solve specific
problems
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The Human Element in Expert
Systems
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Builder/ Knowledge engineer
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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User/novice
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Structure of
Expert Systems
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Development Environment
Consultation Environment
Three Major Components:
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Knowledge Base
Inference Engine
User Interface
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TABLE 12.2 Generic Categories of Expert Systems
Category
Interpretation
Problem Addressed
Inferring situation descriptions from observations
Prediction
Inferring likely consequences of given situations
Diagnosis
Inferring system malfunctions from observations
Design
Configuring objects under constraints
Planning
Developing plans to achieve goal(s)
Monitoring
Comparing observations to plans, flagging
exceptions
Debugging
Prescribing remedies for malfunctions
Repair
Executing a plan to administer a prescribed remedy
Instruction
Diagnosing, debugging and correcting student
performance
Control
Interpreting, predicting, repairing and monitoring
system behaviors
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Benefits of
Expert Systems
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Major Potential ES Benefits
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Increased Output and Productivity
Decreased Decision Making Time
Increased Process(es) and Product Quality
Reduced Downtime
Capture of Scarce Expertise
Flexibility
Easier Equipment Operation
Elimination of the Need for Expensive
Equipment
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– Operation in Hazardous Environments
– Accessibility to Knowledge and Help Desks
– Increased Capabilities of Other Computerized
Systems
– Integration of Several Experts' Opinions
– Ability to 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 CBIS
(provide intelligent capabilities to large CBIS)
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Problems and Limitations of
Expert Systems
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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
Users of expert systems have natural
cognitive limits
ES work well only in a narrow domain of
knowledge
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Types of Expert Systems
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Expert Systems Versus Knowledge-based
Systems
Rule-based Expert Systems
Frame-based Systems
Automated Rule Induction Systems
Case-based Systems
Hybrid Systems
Model-based Systems
Ready-made (Off-the-Shelf) Systems
Real-time Expert Systems
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An Overview of Neural Computing
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Constructing computer systems that mimic
certain processing capabilities of the
human brain
Knowledge representations based on
– Massive parallel processing
– Fast retrieval of large amounts of
information
– The ability to recognize patterns based
on historical cases
Neural Computing = Artificial Neural
Networks (ANNs)
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The Biology Analogy:
Biological Neural Networks
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Neurons: Brain Cells
– Nucleus (at the center)
– Dendrites provide inputs
– Axons send outputs
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Synapses increase or decrease
connection strength and cause
excitation or inhibition of
subsequent neurons
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Input
data
Dendrite
input wire
Neuron #1
Axon
(output wire)
Weight
W1,2
Neuron #2
Dendrite
Axon
Synapse
(control of flow of
electrochemical fluids
Data
signals
Neuron #3
FIGURE 17.3 Three Interconnected Artificial Neurons
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Artificial Neural Networks
(ANN)
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A model that emulates a biological
neural network
Software simulations of the massively
parallel processes that involve
processing elements interconnected in
a network architecture
Originally proposed as a model of the
human brain’s activities
The human brain is much more
complex
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Definition of Intelligent
Agent
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“Intelligent agents are software
entities that carry out some set of
operations on behalf of a user or
another program, with some degree of
independence or autonomy and in so
doing, employ some knowledge or
representation of the user’s goals or
desires.” (“The IBM Agent”
[http://activist.gpl.ibm.com:81/WhitePa
per/ptc2.htm])
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Expert Systems
Examples
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1. XCON (Expert VAX System
Configuration and Mass
Customization)
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Digital Equipment Corp. (DEC)
minicomputer system
configuration
Manually: Complex task, many
errors, not cost effective
Cost savings estimated at about $15
million / year
Literature: Over $40 million / year
later
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2. MYCIN
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To aid physicians in diagnosing meningitis
and other bacterial blood infections and to
prescribe treatment
To aid physicians during a critical 24-48-hour
period after the detection of symptoms, a
time when much of the decision making is
imprecise
Early diagnosis and treatment can save a
patient from brain damage or even death
Stanford Medical School in the 1970s by
Dr. Edward H. Shortliffe
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MYCIN Features
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Rule-based knowledge
representation
Probabilistic rules
Backward chaining method
Explanation
User-friendly system
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3. Gate Assignment Display
System (GADS)
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4. DustPro--Environmental
Control in Mines
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