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Class 13: Expert Systems
July 27th, 2011
 Define
the term “expert system”
 List two examples of expert systems
 List three different types of problem
structures
 Define the term “decision support system”
 Define the term “data mining”
 List 3 practical applications of data mining
 Explain the difference between Descriptive
and Predictive data mining
 List a tool that can help you perform data
mining
 Data

Mining (Today):
Humans and machines generate knowledge
from data
 Decision

Support Systems (Tuesday)
Combining models and data in an attempt to
solve semistructured and some unstructured
problems with extensive user involvement
 Expert
Systems (Machine’s that make
decisions)

Computer systems that attempt to mimic human
experts by applying expertise in a specific
domain.
 List
a few current events in information
systems news
 Define the term “expert system”
 List components that make expert systems
work
 Describe the difference between hill
climbing and a genetic algorithm
 List two examples of expert systems
Expert Systems are computer systems that
attempt to mimic human experts by applying
expertise in a specific domain.
They may be glorified decision support
systems (DSS) – or they may be systems that
take the entire burden of decision making
from their human users.
The transfer of expertise from an expert to a
computer and then to a user involves four
activities:




Knowledge acquisition – obtained from experts;
data mining, etc.
Knowledge representation – stored and
organized as “rules”
Knowledge inferencing – computer is
programmed to make inferences and reason
Knowledge transfer – inferenced expertise is
transferred to user via a recommendation
 Star
Trek Voyager’s doctor: a 24th century
expert system
Species:
Hologram
Appearance:
Human male
Holoprogram:
EMH, ECH
Creator:
Dr. Lewis Zimmerman
Original purpose:
Emergency Medical
Treatment
Occupation:
Chief Medical Officer
Activation date:
2371
Status:
Active (2378)
Spouse(s):
Charlene
Children:
Jason Tebreeze
Jeffrey
Belle
http://memoryalpha.org/wiki/The_Doctor
 TOPIO
How are
expert systems
created?
 Expert
Systems themselves are based on
Artificial Intelligence

Machine learning based on “intelligent”
algorithms
 “Intelligence”
must come from some actual
human observations before they are
programmed into a machine

Rules based on actual human behavior

Computational intelligence is the study of the
design of intelligent agents

An agent is something that acts in an
environment by its own volition (like a human
being)

An intelligent agent is a system that acts
intelligently




appropriate for its circumstances and its goal
flexible to changing environments and changing goals
learns from experience
makes appropriate choices given perceptual
limitations and finite computation
 AI
“knows” environment parameters
 AI
reacts intelligently to attain goal

takes action with highest probable success
 Tic-Tac-Toe
 Deep
Blue – IBM computer designed
as an expert chess player (decision tree)

Beat Gary Kasparov in 1997
Okay so what is going on inside
the machine that “learns” and
becomes an expert?
Let’s look at two algorithms that
look for “best” solutions.
A

formula to decided the best fit?
Highest similarity
 Let’s
look at two algorithms:
 “Hillclimbing”



“Climb” until you reach the highest spot
Next move is based on better alternative
Method of exploitation
 Genetic



Algorithm
Search for a solution by “survival of the fittest”
Use mutation for exploration
Use crossover for exploitation
From where you are, look ahead and look behind. Which
is higher? Go that way.
 Local

Maxima/Minima
(As opposed to GLOBAL maxima/minima)
 If
you start in the “wrong” place on the hill,
you will never reach the top!
Local
Maximum
 Genetic
algorithms are inspired by Darwin's
theory about evolution
A
solution to a problem (like a complicated
math problem) can be solved or “evolved” by
a genetic algorithm

1. A Random Population of Chromosomes is
Generated

2. The “fitness” of each chromosome is
determined (how close the answers are to the
solution)

3. Crossover occurs between chromosomes

4. Mutation occurs between chromosomes

5. New chromosomes go through the same
process until a “best” answer is reached
 When
two chromosomes “mate,” it is often
the BEST chromosomes that get together –
this process is exploitation of the most fit
chromosomes.
 When
mutation occurs, the algorithm
explores alternative chromosomes
other than the “best” ones.
 Fixing


Too much mutation leads to “randomness”
Too much crossover leads “randomness”
 No

the Parameters
mutation?
Could lead to local maxima/minima once again
 Greyhound
 Could
Racing Project
a machine using artificial intelligence
algorithms beat the experts?

Fastest Time: fastest time in seconds for a 5/16 mile race

Win Percentage: number of first places / total races

Place Percentage: number of second places / total races

Show Percentage: number of third places / total races

Break Average: position during first turn

Finish Average: average finishing position

Time 7 Average: average finish time of 7 most recent
races
 Based
on those attributes:
 Each
expert (Human) correctly predicted
only about 20 winners / 100 races and had a
NEGATIVE $70 payoff
 ID3
Algorithm: 34 winners / 100 races, but
DID NOT BET on all of them, so $69 payoff
 Neural
Network: 20 winners / 100 races, but
$124 payoff -- WHY??
XDrawbacks
XExpert Systems don’t learn like experts
Knowledge becomes obsolete.
 Rules don’t adapt to change.

XThey’re impractical for complex
problems.

Geometric growth (2n) is an issue.
Genetic Algorithms do NOT guarantee you
will find the true optimal solution.
Why can’t a GA evolve
an expert?
“Any sufficiently advanced
technology is indistinguishable from
magic.” – Arthur C. Clarke
 Body
language?
 Physiological
 Voice?
 Words?
signs (sweat, heart rate)?
 http://watch.discoverychannel.ca/daily-
planet/november-2010/daily-planet--november-10-2010/#clip373790
 6:20
 Systems
are being developed for Lie
Detection
 To
learn the rules needed to build these
systems, we need to understand the rules of
human behavior – which behaviors signal
deceit and which behaviors signal truth
 Over
a decade of research in unobtrusive
deception detection
 First let’s look at past method for deception
detection
Polygraph invented in
1915 by Harvardtrained Ph.D., LL.B.
William Moulton
Marston
Claimed it could detect
lies by measuring blood
pressure

Fundamental assumption is that physiological
responding:
differs when one is truthful versus being deceptive,
 demonstrates a specific physiological “lie response”


Typically recorded:
Respiration
 Cardiovascular activity (BP, HR)
 Skin resistance


These measures:
provide an indication of changes in autonomic activity
 do not index the lie response

 The
polygraph is incomplete, and physiology
can be sabotaged


Place a tack in your shoe
Think “exciting” thoughts during the test
 Obtrusive
!!! !!! And time consuming
 Need more cues
Unobtrusive Sensing
Deceptive
Method
Truthful
Correct
Error
Undecided
Correct
Error
Undecided
Professional Interviewers
34
15
0
63
22
0
Eye Tracker A2
21
18
0
49
22
0
Eye Tracker A3
10
7
22
28
10
24
Eye Tracker Mug Only
21
18
0
50
21
0
Fused ET & Human
28
11
0
53
18
0
LDV – CIT (Q25 - Q27)
21
10
0
22
15
0
LVA (Fflic) – CIT
10
32
0
62
7
0
Fused LDV & LVA – CIT
20
7
0
21
8
0
Linguistic (LIWC, Q15)
35
13
0
66
18
0
Linguistic (A99A, Q8)
20
24
0
60
18
0
26
16
0
59
10
0
82
32
0
72
42
0
103
12
0
67
47
0
Kinesic (SBT & ASM,
Q23)
Startle Blink A1 (Q3 & Q9
vs Q2, Q4, Q6 & Q8)
Startle Blink A2 (Q3 & Q5
vs Q4 & Q6)
 http://www.liwc.net/liwcresearch07.php
Given a transcript of speech, or written
text, what kinds of words or patterns of
words might indicate deception?
Linguistic Cue
1st
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13
Q14
Person Plural
1st Person Singular
D>T
2nd Person
3rd Person Plural
T>D
T>D
3rd Person Singular
Adverbs
T>D
D>T
Anger
Anxiety
T>D
Auxiliary Verbs
Big Words
T>D
Causation
Certainty
Cognitive Processes
D>T
T>D
D>T
T>D
T>D
T>D
D>T
T>D
Feeling
Future Tense
T>D
T>D
Hearing
Impersonal Pronouns
Insight
T>D
T>D
D>T
D>T
Percentage of Eye Gaze in Area of Interest
Pupil Dilation Differences
 With
testing, the ultimate goal is to reach an
accuracy rate of 90% for both truth and
deception.
 Major
problem? Baselines – how do we know
what the individuals we are scanning are like
“normally?”
 We
will try to combine as many cues as
possible, as deception is complex.
 Certain
parts of the brain are activated when
creativity or imagination are used instead of
rote memory
 Challenge:
Measuring the exact
slice of the brain necessary
every time is difficult





Make models detecting deception
 For example, decision trees
Integrate sensors
Natural language processing
Interface design
Cost, speed, robustness
 Reading:

RT 334-341
 Quiz