1. Chapter Eleven Artificial Intelligence II: Operational Perspective

2. Historical Perspective  Man has long been interested in creating a machine in his own image.  Many mechanical dolls were created in the 18th century.  Cultural: In the opera “The Tales of Hoffman,” the protagonist falls in love with a mechanical doll.

3. 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.

4. Early Successes If we can develop a model of human thinking, then we can build a machine that emulates this process—this evolved into what we call “EXPERT SYSTEMS.” If we can develop a model of human thinking, then we can build a machine that emulates this process—this evolved into what we call “EXPERT SYSTEMS.”

5. Evaluation of the Expert System  Expert System technology has led to many useful applications (e.g., medical diagnosis). However, such systems have an inherent problem. To work properly they need a clear definition of “truth” and “falsity.”

6. A Problem for Expert Systems Suppose one wishes to determine if a train is approaching a station too rapidly. What does one mean by “too rapidly”? An Expert System would need a well- defined answer with specific speed limitations in order to respond properly. There may not be a specific range of numbers defining the term “too rapidly.” Suppose one wishes to determine if a train is approaching a station too rapidly. What does one mean by “too rapidly”? An Expert System would need a well- defined answer with specific speed limitations in order to respond properly. There may not be a specific range of numbers defining the term “too rapidly.”

7. Fuzzy Logic The difficulties with “truth” as noted have given rise to a new category of automata— the FUZZY LOGIC machine. The difficulties with “truth” as noted have given rise to a new category of automata— the FUZZY LOGIC machine. The defining term is an obvious extension of the concept; decision making is “fuzzy” in nature. The defining term is an obvious extension of the concept; decision making is “fuzzy” in nature.

8. Evaluation of Fuzzy Logic While Fuzzy Logic also has great value, engineers and scientists remain “frustrated” because machines based on this concept still do not “behave” like humans. While Fuzzy Logic also has great value, engineers and scientists remain “frustrated” because machines based on this concept still do not “behave” like humans. This has given rise to a third type of AI - the NEURAL NET machine. This has given rise to a third type of AI - the NEURAL NET machine.

9. The Neural Net Concept Build a machine that “replicates” the human brain and “let it think” (and learn) on its own. Can’t we just teach the machine just as our parents taught us to reason when we were young? Build a machine that “replicates” the human brain and “let it think” (and learn) on its own. Can’t we just teach the machine just as our parents taught us to reason when we were young?

10. A Summary of AI “Top Down” (Abstract thinking and logical processes) Formal Logic DeductionInductionAbduction Fuzzy Logic “Bottom Up” (Build a machine that is a “copy” of the brain and let it “think.”) Neural Net

11. 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

12. Architecture of the Expert System INFERENCE ENGINE INTERFACE KNOWLEDGEBASE 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.

13. Expert System Example The Knowledge Base Contains Rules and Facts: The Knowledge Base Contains Rules and Facts: Rule 1: Stop the car when light is red. Rule 2: Allow the car to proceed when light is green. Rule 3: Obey speed limits when driving. Fact 1: Speed of car is 30 MPH. The Inference Engine produces a result using a reasoning process (e.g., deduction). The Inference Engine produces a result using a reasoning process (e.g., deduction). Fact: Speed of car is 30 MPH. Fact: There is a red light ahead. Trigger Rule 1: Stop the car when light is red. Resulting Decision: Car must stop.

14. Fuzzy Logic Paradigm Fuzzy Logic Attempts to Overcome the Vagaries of Truth and Falsity and Better Reflect Human Thinking The “real” world of physical measurements. The “linguistic” world of words Solve the problem in words Translate physical measurements into “equivalent” words Convert word solutions to physical quantities.

15. Belief in Fuzzy Logic age belief that the person is old. -  1.00.80.60.40.20.0 0 10 20 30 40 50 60 70 80 90 100 our ‘confidence’ that an individual aged 30 is old is only 0.2.

16. Fuzzy Rules of Logic A and B = min (µ A, µ B ) A or B = max (µ A, µ B ) Not A = 1 - µ A

17. 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. FuzzyInferenceEngineWaistWeight Diet

18. 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.”)

19. Waist Membership Classes for the Fuzzy Example 1 32 34 36 38 40 42 44 waist NAF NA = normal waist F = fat

20. Weight Membership Classes for the Fuzzy Example NW= normal weight H = heavy 1 100 120 140 160 180 200 220 240 weight NWH

21. Membership Classes for the Rules of the Fuzzy Example 1 20 30 40 50 60 70 80 90 100 diet index M (Rule 2) WL (Rule 1) M = maintenance WL = weight loss 0.40.3

22. 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? 1 32 34 36 38 40 42 44 waist NAF  F =0.7  N =0.3 waist = 37

23. Assessing the Facts for the Weight in the Fuzzy Example 1 100 120 140 160 180 200 220 240 weight NWH µ NW =o.8 µ H =0.4 H = Heavy NW = Normal weight weight = 170

24. Reasoning in Words for the Fuzzy Example Applying Rule 1 Applying Rule 1 (Waist is fat and weight is heavy) The µ of the combination = min (µ H,  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 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.

25. 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 = (28)(0.3) + (55)(0.4) (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.

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