Transcript Chap 11: Artificial Intelligence II: Operational Perspective

Chapter Eleven
Artificial Intelligence II:
Operational Perspective
What is AI?

From one perspective, AI is the study of
automata (machines) that can learn,
understand, interpret, and arrive at
conclusions in a manner considered
intelligent, just as if it were being carried out
by a human.
Some Approaches To AI
“Top Down”
(Abstract thinking
and logical
processes)
Formal Logic
Deduction
Induction
Abduction
Neural Net
Fuzzy Logic
“Bottom Up”
(Build a machine that is a
“copy” of the brain and let
it “think.”)
A Sampling of Applications
Management: Cost estimates, scheduling; intelligent document
retrieval.
Science & Engineering: prediction of chemical reactions; chemical
identifications; equipment configuration; system troubleshooting;
circuit design.
Industrial: process control; mfg. quality control.
Financial/legal: investment strategies; prediction of financial trends;
loan application analysis; real estate price evaluation; estate planning.
Medical: image processing; diagnosis; rehabilitation.
Military and Space: classification of fingerprints; computer security;
signal/target recognition.
Other: language (natural language processing); speech recognition;
prediction of sporting events; handwriting recognition; optical
character recognition
Architecture of the
Knowledge-Based System
INFERENCE
ENGINE
KNOWLEDGE
BASE
INTERFACE
USER
Interface: Allows user to access the system (questions, answers).
Inference Engine: Includes reasoning (Production rules, Logic).
Knowledge Base: Facts and abstract representation of the worldview.
Logic-Based Reasoning Systems
•
See example of SNePS
Expert Systems

Operate in domains in which


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


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There are human novices.
There are human experts.
There are no well-defined “correct” answers.
Novices can become experts.
Novices are trained by experts.
Novices are declared experts by experts.
Production Rule technology often used.
Fuzzy Logic
•
Replaces two-valued (True or False) logic.
Belief in Fuzzy Logic
belief that the person is old.
-
1.0
0.8
0.6
0.4
0.2
our ‘confidence’ that an
individual aged 30 is old is
only 0.2.
0.0
age
0 10 20 30 40 50 60 70 80 90 100
Fuzzy Rules of Logic
A and B = min (µA, µB)
A or B = max (µA, µB)
Not A = 1 - µA
A Fuzzy Example
Dieting—We all know that one has to have proper diet and exercise. In
this case we will consider dieting alone. What we measure are the size
of a person’s waist and the person’s weight; these are the "real world"
variables. Our FL controller is going to recommend the kind of diet that
the person should undertake.
Waist
Fuzzy
Inference
Weight
Engine
Diet
Fuzzy Rules for the Example
Rule 1:
If (waist is “fat”) and (weight is “heavy”) then
(recommend weight loss diet).
Rule 2:
If (waist is “normal”) and (weight is “normal”) then
(recommend maintenance diet).
(A diet index value of 0 means “stuff your face” and a diet
index value of 100 means “prisoner’s starvation.”)
Waist Membership Classes for
the Fuzzy Example
NA = normal
waist
NA
F
F = fat
1
32
34
36
38
40
42
44 waist
Weight Membership Classes for
the Fuzzy Example
NW= normal
weight
NW
H
H = heavy
1
100
120
140
160
180
200
220
240 weight
Membership Classes for the
Rules of the Fuzzy Example
M = maintenance
WL = weight loss
M (Rule 2)
WL (Rule 1)
1
0.4
0.3
20
30
40
50
60
70
80
90
100 diet index
Assessing the Facts for the Waist
in the Fuzzy Example
A person comes to our (very profitable) diet clinic with the following facts:
waist = 37 inches
weight = 170 pounds
What diet should we advise?
NA
F
1
F=0.7
N=0.3
32
waist
34
36
38
40
waist = 37
42
44
Assessing the Facts for the
Weight in the Fuzzy Example
H = Heavy
NW = Normal weight
NW
µNW=o.8
H
1
µH=0.4
100 120
weight
140
160
180
200
220
weight = 170
240
Reasoning in Words for the
Fuzzy Example
• Applying Rule 1
(Waist is fat and weight is heavy)
The µ of the combination = min (µH, F ) = min (0.4, 0.7) = 0.4
We apply this to weight loss and this tells us to recommend a weight loss diet
level index of 55 (see earlier membership curve).
• Applying Rule 2
(waist is normal and weight is normal)
The µ of the combination is min (µ[normal waste], µ[normal weight]) = min(0.3,
0.8) = 0.3
We apply this to the maintenance diet membership class that tells us to
recommend a maintenance diet level index of 28 (see earlier membership
curve).
We appear to be confronted with two “conflicting” recommendations:
Recommend dieting index of 55 and recommend maintenance diet of 28. We
must resolve this and produce “crisp” results.
Finding a Recommendation for
the Fuzzy Example
We must combine the recommendations of Rule 1 and Rule 2 into a single
result. There are several ways to do this; one method is to generate a
weighted average. The weight of each rule action is weighted by the
corresponding membership of its condition and the result is then averaged.
(28)(0.3) + (55)(0.4)
Final dietary recommendation =
(0.4 + 0.3)
 43
43 represents a “moderate” diet somewhere between free range and
starvation. In the real world this could be directly translated into daily
caloric intake.
Evaluation of Fuzzy Logic
• Haack argues that there are very few true candidates for
which Fuzzy Logic is useful. Most problems can be solved
using principles drawn from probability. The computer
programs are much too complicated and thus Fuzzy Logic
serves no useful purpose.
• Fox has rebutted this line of reasoning by noting that FL
is effective when we need to describe real-world
relationships that are “fuzzy.”