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Sunday, July 6, 2008


T 3184

Part –a 
1. Define artificial intelligence
2. What is the use of heuristic functions
3. How to improve the effectiveness of a search based problem solving technique
4. What is constraint satisfaction problem
5. What is unification algorithm
6. How can you represent the resolution in predicate logic
7. List out the advantages of non monotonic reasoning
8. Differentiate between JTMS and LTMS
9. What are framesets and instances
10. List out the important components of a script

Part –b
11. A) 
i) give an example of a problem for which breadth first search would work better than depth first search.
II) explain the algorithm for steepest hill climbing 
b) explain the following search strategies 
i) best first search
ii) A* search

12. A) explain min-max search procedure
b) describe alpha-beta pruning and give the other modifications to the minmax procedure to improve its performance.

13. A) illustrate the use of predicate logic to represent the knowledge with suitable example.
b) consider the following sentences
• John likes all kinds of food
• Apples are food
• Chicken is food
• Anything anyone eats and isn’t killed by is food
• Bill eats peanuts and is still alive
• Sue eats everything bill eats
i) translate these sentences into formulas in predicate logic
ii) prove that john likes peanuts using backward chaining
iii) convert the formulas of a part into clause form 
iv) prove that john likes peanuts using resolution 
14. a) i) with an example the logics for non monotonic reasoning 
b) explain how Bayesian statistics provides reasoning under various kind s of uncertainty.

15). A) i) construct semantic net representations for the following .
• pomepeian (marcus), blacksmith(marcus)
• mary gave the green flowered vase to her favorite cousin.
ii)construct partitioned semantic net representations for the following :
• every batter hit a ball 
• all the batters like the pitcher 
b). illustrate the learning from examples by induction with suitable example.

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