Chapter ElevenChapter Eleven

Artificial Intelligence II: Artificial Intelligence II: Operational PerspectiveOperational Perspective

What is AI?What is AI?

From one perspective, AI is the study of From one perspective, AI is the study of automata (machines) that can learn, automata (machines) that can learn, understand, interpret, and arrive at understand, interpret, and arrive at conclusions in a manner considered conclusions in a manner considered intelligent, just as if it were being carried out intelligent, just as if it were being carried out by a human.by a human.

Some Approaches To AISome Approaches To AI

““Top Down”Top Down”(Abstract thinking (Abstract thinking

and logical and logical processes)processes)

Formal LogicFormal Logic

DeductionDeductionInductionInductionAbductionAbduction

Fuzzy LogicFuzzy Logic

““Bottom Up”Bottom Up”(Build a machine that is a (Build a machine that is a “copy” of the brain and let “copy” of the brain and let

it “think.”)it “think.”)

Neural NetNeural Net

A Sampling of ApplicationsA Sampling of Applications

ManagementManagement: Cost estimates, scheduling; intelligent document : Cost estimates, scheduling; intelligent document retrieval.retrieval.Science & EngineeringScience & Engineering: prediction of chemical reactions; chemical : prediction of chemical reactions; chemical identifications; equipment configuration; system troubleshooting; identifications; equipment configuration; system troubleshooting; circuit design.circuit design.Industrial:Industrial: process control; mfg. quality control. process control; mfg. quality control.Financial/legalFinancial/legal: investment strategies; prediction of financial trends; : investment strategies; prediction of financial trends; loan application analysis; real estate price evaluation; estate planning.loan application analysis; real estate price evaluation; estate planning.MedicalMedical: image processing; diagnosis; rehabilitation.: image processing; diagnosis; rehabilitation.Military and SpaceMilitary and Space: classification of fingerprints; computer security; : classification of fingerprints; computer security; signal/target recognition.signal/target recognition.OtherOther: language (natural language processing); speech recognition; : language (natural language processing); speech recognition; prediction of sporting events; handwriting recognition; optical prediction of sporting events; handwriting recognition; optical character recognitioncharacter recognition

Architecture of theArchitecture of theKnowledge-Based SystemKnowledge-Based System

INFERENCE INFERENCE ENGINEENGINE

INTERFACEINTERFACE

KNOWLEDGEKNOWLEDGE

BASEBASE

USERUSER

Interface: Allows user to access the system (questions, answers).Interface: Allows user to access the system (questions, answers).Inference Engine: Includes reasoning (Production rules, Logic).Inference Engine: Includes reasoning (Production rules, Logic).Knowledge Base: Facts and abstract representation of the worldview.Knowledge Base: Facts and abstract representation of the worldview.

Logic-Based Reasoning SystemsLogic-Based Reasoning Systems

• See example of SNePSSee example of SNePS

Expert SystemsExpert Systems

Operate in domains in whichOperate in domains in which There are human novices.There are human novices. There are human experts.There are human experts. There are no well-defined “correct” answers.There are no well-defined “correct” answers. Novices can become experts.Novices can become experts. Novices are trained by experts.Novices are trained by experts. Novices are declared experts by experts.Novices are declared experts by experts.

Production Rule technology often used.Production Rule technology often used.

Fuzzy LogicFuzzy Logic

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

Belief in Fuzzy LogicBelief in Fuzzy Logic

ageage

belief that the person is old. belief that the person is old.

- -

1.01.0

0.80.8

0.60.6

0.40.4

0.20.2

0.00.0

0 10 20 30 40 50 60 70 80 90 1000 10 20 30 40 50 60 70 80 90 100

our ‘confidence’ that an our ‘confidence’ that an individual aged 30 is old is individual aged 30 is old is only 0.2.only 0.2.

Fuzzy Rules of LogicFuzzy Rules of Logic

A and B = min (A and B = min (µµAA, µ, µBB))

A or B = max (A or B = max (µµAA, µ, µBB))

Not A = 1 - Not A = 1 - µµAA

A Fuzzy ExampleA Fuzzy Example

Dieting—We all know that one has to have proper diet and exercise. In 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 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" 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 variables. Our FL controller is going to recommend the kind of diet that the person should undertake.the person should undertake.

Fuzzy Fuzzy

InferenceInference

EngineEngine

WaistWaist

WeightWeight

Diet Diet

Fuzzy Rules for the ExampleFuzzy Rules for the Example

Rule 1: Rule 1: If (waist is “fat”) and (weight is “heavy”) then If (waist is “fat”) and (weight is “heavy”) then (recommend weight loss diet).(recommend weight loss diet).

Rule 2:Rule 2:If (waist is “normal”) and (weight is “normal”) then If (waist is “normal”) and (weight is “normal”) then (recommend maintenance diet).(recommend maintenance diet).

(A diet index value of 0 means “stuff your face” and a diet (A diet index value of 0 means “stuff your face” and a diet index value of 100 means “prisoner’s starvation.”)index value of 100 means “prisoner’s starvation.”)

Waist Membership Classes for Waist Membership Classes for the Fuzzy Examplethe Fuzzy Example

11

32 34 36 38 40 42 44 waist32 34 36 38 40 42 44 waist

NANA FF

NA = normal NA = normal waistwaist

F = fatF = fat

Weight Membership Classes for Weight Membership Classes for the Fuzzy Examplethe Fuzzy Example

NW= normal NW= normal weightweight

H = heavyH = heavy11

100 120 140 160 180 200 220 240 weight100 120 140 160 180 200 220 240 weight

NWNW HH

Membership Classes for the Membership Classes for the Rules of the Fuzzy ExampleRules of the Fuzzy Example

11

20 30 40 50 60 70 80 90 100 diet index20 30 40 50 60 70 80 90 100 diet index

M (Rule 2)M (Rule 2) WL (Rule 1)WL (Rule 1)

M = maintenanceM = maintenanceWL = weight lossWL = weight loss

0.40.4

0.30.3

Assessing the Facts for the Waist Assessing the Facts for the Waist in the Fuzzy Examplein the Fuzzy Example

A person comes to our (very profitable) diet clinic with the following facts:A person comes to our (very profitable) diet clinic with the following facts:waist = 37 incheswaist = 37 inchesweight = 170 poundsweight = 170 pounds

What diet should we advise?What diet should we advise?

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32 34 36 38 40 42 44 waist32 34 36 38 40 42 44 waist

NANA FF

FF=0.7=0.7

NN=0.3=0.3

waist = 37waist = 37

Assessing the Facts for the Assessing the Facts for the Weight in the Fuzzy ExampleWeight in the Fuzzy Example

11

100 120 140 160 180 200 220 240 weight100 120 140 160 180 200 220 240 weight

NWNW HH

µµNWNW=o.8=o.8

µµHH=0.4=0.4

H = HeavyH = Heavy

NW = Normal weightNW = Normal weight

weight = 170weight = 170

Reasoning in Words for the Reasoning in Words for the Fuzzy ExampleFuzzy Example

• Applying Rule 1Applying Rule 1(Waist is fat and weight is heavy)(Waist is fat and weight is heavy)The The µ of the combination = min (µµ of the combination = min (µH,H, FF ) = min (0.4, 0.7) = 0.4 ) = min (0.4, 0.7) = 0.4We apply this to weight loss and this tells us to recommend a weight loss diet We apply this to weight loss and this tells us to recommend a weight loss diet level index of 55 (see earlier membership curve).level index of 55 (see earlier membership curve).

• Applying Rule 2Applying Rule 2(waist is normal and weight is normal)(waist is normal and weight is normal)The µ of the combination is min (µ[normal waste], µ[normal weight]) = min(0.3, The µ of the combination is min (µ[normal waste], µ[normal weight]) = min(0.3, 0.8) = 0.30.8) = 0.3We apply this to the maintenance diet membership class that tells us to 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).recommend a maintenance diet level index of 28 (see earlier membership curve).

We appear to be confronted with two “conflicting” recommendations: We appear to be confronted with two “conflicting” recommendations: Recommend dieting index of 55 and recommend maintenance diet of 28. We Recommend dieting index of 55 and recommend maintenance diet of 28. We must resolve this and produce “crisp” results.must resolve this and produce “crisp” results.

Finding a Recommendation for Finding a Recommendation for the Fuzzy Examplethe Fuzzy Example

We must combine the recommendations of Rule 1 and Rule 2 into a single 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 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 weighted average. The weight of each rule action is weighted by the corresponding membership of its condition and the result is then averaged.corresponding membership of its condition and the result is then averaged.

Final dietary recommendation = Final dietary recommendation = (28)(0.3) + (55)(0.4)(28)(0.3) + (55)(0.4)

(0.4 + 0.3)(0.4 + 0.3) 4343

43 represents a “moderate” diet somewhere between free range and 43 represents a “moderate” diet somewhere between free range and starvation. In the real world this could be directly translated into daily starvation. In the real world this could be directly translated into daily caloric intake.caloric intake.

Evaluation of Fuzzy LogicEvaluation of Fuzzy Logic

• Haack argues that there are very few true candidates for Haack argues that there are very few true candidates for which Fuzzy Logic is useful. Most problems can be solved which Fuzzy Logic is useful. Most problems can be solved using principles drawn from probability. The computer using principles drawn from probability. The computer programs are much too complicated and thus Fuzzy Logic programs are much too complicated and thus Fuzzy Logic serves no useful purpose.serves no useful purpose.

• Fox has rebutted this line of reasoning by noting that FL Fox has rebutted this line of reasoning by noting that FL is effective when we need to describe real-world is effective when we need to describe real-world relationships that are “fuzzy.”relationships that are “fuzzy.”