Chapter Eleven

1
Chapter Eleven

• Artificial Intelligence II Operational
  Perspective

2
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.

3
Some Approaches To AI
4
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
5
Architecture of theKnowledge-Based System
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.
6
Logic-Based Reasoning Systems

• See example of SNePS

7
Expert Systems

• Operate in domains in which
• 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.

8
Fuzzy Logic

• Replaces two-valued (True or False) logic.

9
Belief in Fuzzy Logic
10
Fuzzy Rules of Logic
A and B min (µA, µB)
A or B max (µA, µB)
Not A 1 - µA
11
A Fuzzy Example
DietingWe 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
persons waist and the persons weight these are
the "real world" variables. Our FL controller is
going to recommend the kind of diet that the
person should undertake.
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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 prisoners
starvation.)
13
Waist Membership Classes for the Fuzzy Example
14
Weight Membership Classes for the Fuzzy Example
15
Membership Classes for the Rules of the Fuzzy
Example
16
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
?F0.7 ?N0.3
32 34 36 38
40 42 44 waist
waist 37
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Assessing the Facts for the Weight in the Fuzzy
Example
H Heavy NW Normal weight
NW
H
1
µNWo.8 µH0.4
100 120 140 160 180 200
220 240 weight
weight 170
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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 (µnormal waste,
  µnormal 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.

19
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. Final dietary recommendation
? 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.
20
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.