ISMN 3140 (AI/ES)

scarfpocketAI and Robotics

Oct 24, 2013 (3 years and 9 months ago)

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1

This PowerPoint Presentation can
be downloaded/viewed from my
homepage at this web address:

http://www.auburn.edu/~fordfn1/

From there, click
Courses
, then the link:
MNGT 3140


AI/ES

F. Nelson Ford, Ph.D.

Coordinator, MIS Programs

Department of Management

2

Knowledge
-
based
Decision Support and
Artificial Intelligence


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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

3

AI Concepts and Definitions

AI Involves Studying Human
Thought Processes (to Understand
What Intelligence Is) and
Representing Thought Processes
on Machines

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

4

Artificial Intelligence


Artificial intelligence

is
behavior

by
a machine that, if performed by a
human being, would be called
intelligent

(well
-
publicized)



Artificial intelligence Deals Primarily
with
Symbolic
,
Nonalgorithmic

Methods of Problem Solving


Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

5

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

(Continued on next page)

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

6

Signs of Intelligence
(cont’d)


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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

7

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

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

8

Commercial Advantages of
AI Over Natural Intelligence


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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

9

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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

10

The Artificial Intelligence
Field



Involves Many Different Sciences
and Technologies


Linguistics


Psychology


Philosophy


Computer Science


Electrical Engineering


Hardware and Software

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

11

Major AI Areas


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)


Intelligent Agents

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

12

Fundamentals of Expert
Systems


CATS
-
1 at General Electric


The Problem:

General Electric's (GE)

Top Locomotive Field Service
Engineer was Nearing
Retirement

13

Introduction


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

14

The Human Element in Expert
Systems



Builder/ Knowledge engineer


The Expert


Has the special knowledge,
judgment, experience and methods
to
give advice

and
solve problems


Provides knowledge about task
performance


User/novice

15

Structure of

Expert Systems


Development Environment


Consultation Environment


Three Major Components:


Knowledge Base


Inference Engine


User Interface


16

17

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Benefits of

Expert Systems



Major
Potential

ES Benefits


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

19


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)

20

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


Users of expert systems have natural
cognitive limits


ES work well only in a narrow domain of
knowledge

21

Types of Expert Systems


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

22

An Overview of Neural Computing



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)

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

23

The Biology Analogy:


Biological Neural Networks



Neurons: Brain Cells


Nucleus (at the center)


Dendrites provide inputs


Axons send outputs


Synapses increase or decrease
connection strength and cause
excitation or inhibition of
subsequent neurons

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

24

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

25

Artificial Neural Networks
(ANN)


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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

28

Definition of Intelligent
Agent



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

29

Expert Systems
Examples

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1. XCON (Expert VAX System
Configuration and Mass
Customization)


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

31

2. MYCIN


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

32

MYCIN Features


Rule
-
based knowledge
representation


Probabilistic rules


Backward chaining method


Explanation


User
-
friendly system

33

3. Gate Assignment Display
System (GADS)

34

4. DustPro
--
Environmental
Control in Mines