B.E. (Computer Engineering) Eighth Semester (C.B.S.)

Elective - IV : Expert System Design (ESD) TKN/KS/16/7719

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Time : Three Hours Max. Marks : 80 _____________________________________________________________________ Notes : 1. 2. 3. 4. 5. 6. 7. 8. 9.

b)

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a)

Explain in detail the structure of an expert system with a suitable diagram. OR Write notes on knowledge acquisition techniques in expert system.

b)

Discuss the MYCIN expert system focusing on the certainity factor used.

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a)

Consider the following sentences. i) John likes all kinds of food. ii) Apples are food. iii) Chicken is food. iv) Anything anyone eats & Isn't by is food. v) Bill eats peanuts & is still alive. vi) Due eats everything Bill eats. Translate these sentences into predicates logic. Using resolution answer the question ''What food does Due eat?''

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b)

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a)

Explain in detail the basic principles of resolution. Explain resolution algorithm in predicate logic. OR Difference between propositional logic and predicate logic with example.

b)

Explain knowledge representation using frames & semantic networks in details.

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a)

What is production system? How the knowledge represented in production system?

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b)

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a)

Explain in detail Inference Rules & Inference procedure. OR What is mean by pattern recognition in expert system.

b)

Explain inference in production system with top-down inference.

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a)

Describe the frame structure of news collection system.

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What are expert systems? Give the five names of expert system.

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Solve Question 1 OR Questions No. 2. Solve Question 3 OR Questions No. 4. Solve Question 5 OR Questions No. 6. Solve Question 7 OR Questions No. 8. Solve Question 9 OR Questions No. 10. Solve Question 11 OR Questions No. 12. Due credit will be given to neatness and adequate dimensions. Assume suitable data wherever necessary. Illustrate your answers wherever necessary with the help of neat sketches.

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b)

Construct partitioned semantic nets. i) Every batter hits a ball. ii) All the batters like the pitches.

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b)

Explain in details tree like frame taxonomies.

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a)

Explain Bayes theorem with example.

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b)

What is Dempster-Shafer theory.

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OR Explain certainty factor with measure of belief & disbelief with example?

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b)

Write short note on network model.

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a)

Explain Biological concept of neural network.

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Differentiate between supervised and unsupervised learning? OR Explain single layer perceptron?

b)

What is Hybrid intelligence?

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Explain frames with multiple inheritance.

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