INFSY540
Information Resources in
Management
Lesson 9
Chapter 10
Artificial Intelligence & Expert Systems
Learning Objectives
Define “artificial intelligence” (AI)
Identify the major types of AI systems &
provide an example of each
List the characteristics and basic components
of expert systems
Identify at least 3 factors to consider in
evaluating the development of an expert
system
Outline & explain the steps in developing an
expert system
Chapter 10
Slide
2
How do AI persons think?
What is AI?
What is I?
Chapter 10
Slide
3
Characteristics of
Intelligence
Ability to Communicate
Creativity
Internal Knowledge
Ability to Learn
World Knowledge
Goal-Directed Behavior
Self Awareness
Chapter 10
Slide
4
A Hierarchical Model of
Intelligence
Wisdom
Knowledge
Information
Data
Chapter 10
+
Vision
+ Experience
+
Context
Slide
5
What is Artificial Intelligence?
Good Question. There is no generally
accepted definition of Artificial Intelligence.
Why?
In practice, it is an “umbrella term”
 It is multidisciplinary
 Technologies regularly enter and exit the AI
“umbrella”

Chapter 10
Slide
6
AI is a Multi-Disciplinary Field
Psychology
Cognitive
Science
Artificial Computer
Intelligence Science
Linguistics
Chapter 10
Historically, AI practitioners
came from diverse
backgrounds in both
“hard” and “soft” sciences.
Engineering
What other
disciplines have
been involved in AI?
Slide
7
Brief History of AI
1943 McCulloch & Pitts paper on neurons
1950 Age of computer simulation begins
1956 Cognitive AI & Neural Computing fields begin
(Dartmouth Summer Research Conference)
1957 Rosenblatt’s Perceptron
1959 Widrow & Hoff’s MADALINE
1960’s Growth, Progress and Excessive Hype in all of AI
1969 Minsky & Papert’s critique of Perceptrons
(Results in stunted growth of Neural Networks:
1969-1984)
1986 Re-birth of Neural Networks
1997 Deep Blue defeats reigning chess grandmaster
Chapter 10
Slide
8
Turing’s Test
Can the human on the left tell whether the output is
coming from the computer or the human on the right?
Chapter 10
Slide
9
Features of Artificial
Intelligence
The use of computers to do symbolic reasoning
A focus on problems that do not respond to algorithmic
solutions
Problem solving using inexact, missing, or poorly defined
information
An effort to capture and manipulate the significant qualitative
features of a situation rather than relying on numerical
methods
Chapter 10
Slide
10
Features of Artificial
Intelligence
An attempt to deal with issues of semantic meaning as well
as syntactic form
Answers that are neither exact or optimal, but are in some
sense “sufficient”
The use of large amounts of domain-specific knowledge in
solving problems
The use of meta-level knowledge to effect more
sophisticated control of problem-solving strategies
Chapter 10
Slide
11
Application Categories
Interpretation
Prediction
Diagnosis
Design
Planning
Monitoring
Debugging
Repair
Instruction
Control
Optimization
Chapter 10
Inferring situation from observations
Inferring likely consequences of situation
Inferring malfunctions
Configuring objects under constraints
Developing plans to achieve goals
Comparing observations to plans
Prescribing remedies for malfunctions
Executing a plan to administer a remedy
Diagnosing and correcting performance
Managing system behavior
Finding “best” solutions to problems
Slide
12
Some AI Technologies
Expert Systems
Neural Networks
Genetic Algorithms
Fuzzy Logic
Robotics
Natural-Language Processing
Intelligent Tutorials
Computer Vision
Virtual Reality
Game Playing
Chapter 10
Slide
13
Some AI Technologies
Expert Systems: Diagnose, respond & act like a human expert
Neural Networks: Use data to predict outputs or interpret inputs
Genetic Algorithms: Use data to find “optimal” solutions
Fuzzy Logic: Facilitate solutions to human vagueness problems
Robotics: Mimic physical human processes
Natural-Language Processing: Mimic human communication
Intelligent Tutorials: Facilitate human learning
Computer Vision: Mimic human sensory(visual) process
Virtual Reality: Mimic human reality inside a computer
Game Playing: Beat humans in games, e.g. chess
Chapter 10
Slide
14
Cognitive vs Biological AI
Cognitive-based Artificial Intelligence
Top Down approach
 Attempts to model psychological processes
 Concentrates on what the brain gets done

Biological-based Artificial Intelligence
Bottom Up approach
 Attempts to model biological processes
 Concentrates on how the brain works

Chapter 10
Slide
15
Cognitive vs Biological AI
Cognitive AI Tools:

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




Expert Systems
Natural Language
Fuzzy Logic
Intelligent Agents
Intelligent Tutorials
Planning Systems
Virtual Reality
Biological AI Tools







Chapter 10
Neural Networks
Speech Recognition
Computer Vision
Genetic Algorithms
Evolutionary
Programming
Machine Learning
Robotics
Slide
16
What is Artificial Intelligence?
Some definitions of AI:

Eugene Charniak, “...the study of mental faculties through
the use of computational models.”

Patrick Winston, “...the study of computations that make it
possible to perceive, reason, and act.”

Steven Tanimoto, “...computational techniques for
performing tasks that apparently require intelligence when
performed by humans.”

David Parnas, “Artificial intelligence is to artificial flowers
as natural intelligence is to natural flowers.”
Chapter 10
Slide
17
Categories of AI Definitions
Systems that:
Think like humans
Act like humans
rationally
Chapter 10
Think rationally
Act
Slide
18
What is Artificial Intelligence?
Artificial Intelligence: the art of making computers that
behave like the ones in movies”
Bill Bulko
Computers are useless. They can only give you answers.
Pablo Picasso
Computers make it easier to do a lot things, but most of the
things they make easier to do, don’t need to be done.
Andy Rooney
The question of whether a computer can think is no more
interesting than the question of whether a submarine can
swim.
Edgar W. Dijkstra
Chapter 10
Slide
19
Questions?
Suppose we develop an AI program so that it can
score 200 on a standard IQ test. Would we then
have a program more intelligent than a human?
“Surely computers cannot be intelligent-they can
only do what their programmers tell them.” Is the
latter statement true and does it imply the former?
Chapter 10
“Surely animals cannot be intelligent-they can
only do what their genes tell them.” Is the latter
statement true and does it imply the former?
Slide
20
Predicting the Future:
Mission Impossible?
I think there’s a world market for about 5 computers.
Thomas J. Watson, Chairman of the Board, IBM, 1948
There is no reason for any individual to have a
computer in his home.
Ken Olson, President, Digital Equipment, 1977
Chapter 10
Slide
21
Future AI Technologies
Will need to do more than just mimic humans to
improve computer intelligence.

For example, examine products for defects under light
and sound frequencies that human experts cannot
observe.
Will need to focus on creating computer programs
that can learn and teach other computer programs.
Chapter 10
Slide
22
Future AI Technologies
Automatic Programming
Evolutionary Programming
Knowledge Based Systems
Biological Artificial Neural Networks
Real Time Planning and Re-Planning Systems
Intelligent “learning” Agents
Micro, mini and nano robots
Biometric Security Systems
Quantum computing
Chapter 10
Slide
23
Why Should We
Care about AI?
Moving from the industrial age to the
information age has created a whole new world
of problems. There are many very difficult
problems in this new world that an AI way of
thinking might help solve.
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Chapter 10
Information overload problems.
Operations in hazardous environments.
Distributing scarce corporate knowledge.
Problems requiring multidisciplinary teams.
Slide
24
Any questions?
Chapter 10
Slide
25
Knowledge Based Systems (KBS)
and
Expert Systems (ES)
Expert System
A model and associated procedure that exhibits, within
a specific domain, a degree of expertise in problem
solving that is comparable to that of a human expert.
(From Introduction to Expert Systems by Ignizio)
 An expert system is a computer system which emulates
the decision-making ability of a human expert. (From
Expert Systems: Principles and Programming by
Giarratano and Riley)
 Problem solving programs that usually have an
explanation facility and are rich in heuristics.

Chapter 10
Slide
27
Characteristics of an Expert System
Can explain reasoning
Can provide portable knowledge
Can display “intelligent” behavior
Can draw conclusions from complex
relationships
Can deal with uncertainty
Chapter 10
Slide
28
What distinguishes a KBS
from an expert system?
Size of the knowledge base
Reuse of the knowledge
Generality of the knowledge
Large-scale integrated architectures
with multiple reasoning strategies
Chapter 10
Slide
29
Why use a KBS or ES?
Preserve knowledge--builds up the corporate
memory of an organization.
Makes expertise more widely available, even
if scarce or expensive.
Frees expert from repetitive, routine tasks.
Aids in imparting expertise to novices.
Improves worker productivity.
Explore alternatives -- provides a second
opinion in critical situations.
Chapter 10
Slide
30
When to use a KBS or ES?
Domain is knowledge intensive, and can be
modeled with logical rules
Not a natural-language intensive problem
Neither creativity nor physical skills are
required
Optimal results are not required
Subject matter experts are available for
knowledge acquisition
Chapter 10
Slide
31
When to use a KBS or ES?
High payoff
Preserve scarce expertise
Distribute expertise
Provide more consistency than humans
Faster solutions than humans
Training expertise
Chapter 10
Slide
32
Components of KBS and ES
Essential
Knowledge base
 Inference engine

Supporting
KB editor
 Query interface
 Explanation system

Chapter 10
Slide
40
Fig 11.7
Chapter 10
Slide
41
Inference Engine
Human reasoning inspires similar
reasoning strategies in AI:
Classification
 Rules
 Heuristics
 Prior cases
 Expectations

Chapter 10
Slide
43
Classification
We create and use categories to
organize knowledge
Animal
Vertebrate
Reptile
Amphibian
Invertebrate
Mammal
Fish
Chapter 10
Slide
44
Rules
Mostly take the form IF-THEN
Rules can be cascaded, nested
"If A then B" . . .
 "If B then C"
 A-->B-->C

Order of evaluation may matter
Chapter 10
Slide
45
Heuristics
“Rules of thumb”
Heuristics can be captured using rules

"If the meal includes red meat


Then choose red wine"
If the TV reception is bad

Then jiggle the antenna
Can be extremely helpful in AI
applications
Chapter 10
Slide
46
Prior Cases
Exemplified in case-based reasoning

e.g. legal precedents
Similarity of current case to previous
cases provides basis for action choice
Cases stored and retrieved based on
features and structure
Similarities and differences are the
basis for reasoning
Chapter 10
Slide
47
Inference Engine
Controls overall execution of the “rules”.
Descriptions of the Strategies

Forward Chaining
Derive new facts from existing facts
 “Who killed the cat?”


Backward Chaining
Ask if a particular hypothesis is valid.
(Goal-directed inference)
 “Did curiosity kill the cat?”

Chapter 10
Can combine the strategies
Slide
50
Knowledge Base
Uses a representation language to formalize knowledge
Context: Organizes domain into a model of entities and
relationships that make up that domain.
Rules: Logical statements that govern the inference about
the entities and relationships


Chapter 10
attempt to replicate the thought process used by the expert.
Two methods of designing the rules: Rule-Based Reasoning
and Case-Based Reasoning
Slide
53
Knowledge Base
Rule-based Reasoning
 Uses logical rules to guide inference.
1. If you are 150 yds. away and in the fairway, then
select the 7-iron.
 2. If you are in the rough, then use the next lowernumbered club.



Chapter 10
If you start with (150yds, rough), then by applying
the above two rules you will get 6-iron as output.
The rules operate on beliefs and assumptions in the
reasoning context
Slide
54
Knowledge Base
Case-based Reasoning
 Look at all related facts as a “case”, seek to find
similar cases to guide inference
 Reason based on the similarities and differences.
 Example, 1st step, using same problem:




Chapter 10
Case 1: 170 yds., in fairway; used a 5-iron.
Case 2: 160 yds., in fairway; used a 6-iron.
Case 3: 150 yds., in fairway; used a 7-iron.
(150 yds., rough) is probably closest to Case 3.
Slide
55
Knowledge Base
Case-based Reasoning (second step):

Apply rules about what doesn’t match the case:



Chapter 10
a. If the situation is “fairway” and the case is for
“rough”, then use the next higher-numbered club.
b. If the situation is “rough” and the case is for
“fairway”, then use the next lower-numbered club.
Since the situation is “rough” and Case 3 (the best
matching case) is for “fairway”, we would apply
the b. rule above to derive our answer of 6-iron.
Slide
56
Knowledge Base
Rule-Based and Case-Based Reasoning are
equivalent: Any rule-based system can be
rewritten in case-based form, and vice versa.
Using one over the other depends on how the
experts do their job:


Chapter 10
Rule-based: Do they look at one piece of data
at a time?
Case-based: Do they generally reason about
the data in a “big picture” way?
Slide
57
Applications of
Expert Systems & KBS
Credit granting
Shipping
Information management & retrieval
Embedded systems
Help desks & assistance
Chapter 10
Slide
60
Application Categories:
Interpretation
Urban Search and Rescue robots

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
Chapter 10
Interprets information about collapsed buildings
Helps identify potential locations of trapped
victims.
ES is programmed into the robot exploring the
inside of the building looking for “void spaces”.
 Colorado School of Mines
Slide
61
Application Categories:
Interpretation
Bridge Classification

The “Smart Bridge” project allows planners to
classify bridges according to capacity:
Load Classification (weight, throughput,...)
 Clearance Restrictions


Chapter 10
Operates using remote imagery (photographs,
satellite images)
Slide
62
Application Categories:
Diagnosis & Repair
Turbine Engine Vibration Diagnosis
Takes acoustic spectrum from a running a
turbine engine.
 Irregular components of the signal patterns are
identified.
 Mechanic is pointed towards possible faults.

Chapter 10
Slide
63
The US Army AI Center’s
Favorite Photo
The single locked box at the soldier’s feet replaces the stack of
Slide 64
manuals and the tower of test equipment shown.
Chapter 10
Limitations of
Knowledge Based Systems
Limited to narrow problems
Not widely used or tested
Hard to use
Cannot easily deal with “mixed” knowledge
Possibility of error
Cannot refine own knowledge base
Hard to maintain
Possible high development costs
Raise legal & ethical concerns
Chapter 10
Slide
71
Advantages of Expert
Systems Shells and Products
Easy to develop & modify
Use of satisficing
Use of heuristics
Development by knowledge engineers
& users
Chapter 10
Slide
72
Procedural Computing
Conventional software programming
paradigm relies on procedural
computing over data:

Program = Algorithm + Data

Chapter 10
Algorithm is a series of tasks that the computer
must perform, such as:
 read a number
 multiply by 10
 display the result
Slide 73
 etc…
How Do Expert Systems Differ from
Conventional Programs?
As a model of human cognition?
From a programming perspective?
In their performance?
Ability to provide justification?
Relationship to expert behavior?
Are expert systems intelligent?
Chapter 10
Slide
74