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Course : Artificial Intelligence Semester : 1st semester 2005/2006 Course Instructor : Dr. Nabil Hewahi Exercise Sheet 1. Define the following terms AI Expert System Heuristic Search 2. Knowledge Based System Machine Learning Distinguish between Hill climbing OR graph and and Generate and test search techniques AND-OR graph 3. What is the difference between A* and AO* 4. Propose a heuristic function for the water jug problem. 5. Given the following figure R 5 2 P Q 3 6 9 B A 6 15 7 D C E 3 3 3 8 1 F Assume the starting node is R and the goal is F apply A* algorithm 6. Given the following figure R P A Q B C D E 5 F 4 G2 H6 I1 Assume the starting node is R. Using AO* show the solution path direction 1 7. 8. 9. What we mean when we say “ Some solution steps can be ignored or undone” 10. What is the minimax algorithm and when should we use it. 11. Propose a heuristic function for the nim game. 12. For the TIC-TAC-TO, given the heuristic function E(n) = m(n)-O(n), where M(n) is the total number of possible winning for me and O(n) is the total of wins to the opponent. E(n) is the total evaluation for state n. If we explore a) 5 levels for the node b) 7 levels for the node Show the solution path applying minimax algorithm. 13. Consider the following tree Min A B E 5 C F 2 D G H 9 K I J 6 L 4 M 1 O 8 N 5 P 7 a) Suppose A is the minimum player, what is the first move that should be chosen by him. b) What nodes would not need to be examined using Alpha-Beta pruning procedure. 14. Is the minimax algoritm depth first search or breadth first search. 15. What is the difference between propositional calculus and predicate calculus. 16. Consider the following sentences a. John likes all kinds of food Apples are food Chicken is food Anything anyone eats and it is not killed by is food Bill eats peanuts and is still alive Sue eats everything Bill eats Translate these sentences into formulas in predicate logic 2 b. c. d. Convert the formulas of steps a into clause form. Prove that John likes peanuts using resolution Use resolution to answer the question “What food does Sue eat?” 17. What is the difference between monotonic and non-monotonic reasoning. 18. In what applications TMS is considered to be a useful tool. 19. 20. 21. 22. Using MYCIN ‘s rules for inexact reasoning, compute CF, MB, and MD of h1 given three observations where MB(h1,O1) = 0.5 MB(h2,O2) = 0.3 MB(h1,O3)=0.2 What are the requirements to use Bayes theorem . What is the difference between semantic nets and frames (explain with examples). What is expert system tool. 23. “ Support facilities is one of the key features of ES tools” discuss this statement. 24. Can we design ES using a certain tool that supports both forward and backward chaining of inference. 25. a)What is fuzzy logic and what is the advantage of membership functions. b)Can you state some applications where fuzzy logic can be used. c) d) What is the difference between KAS and Prospector. Apply specific to general search of the version space learning the concept table. The provided examples are : + : obj (small, brown,table) + : obj(large,brown,table) +: obj(large,white,table) -:obj(small,green,table) e) Apply the candidate elimination algorithm learning concept “brown table”. The provided examples are + : obj(small,brown,table) + : obj(large,brown,table) -: obj(small,yellow,chair) -:obj(large,brown,cube) f) What are the alternate terms for neural networks 3 g) Given the following data X1 x2 output 1.0 7.2 5.2 3.3 8.1 2.1 1.0 3.5 2.1 3.4 2.3 1.3 1 1 -1 -1 1 -1 Apply few steps to modify the weights using the perceptron to classify the output. 29. Can we solve XOR problem using interpolation net, if yes apply it ( construct the net), if no why ? 30. Can we use backpropagation algorithm in two layers neural network. 31. Apply Entropy measures homogenetiy of examples to construct the decision tree, then extract the rules for the following database. Day D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 Outlook Sunny Sunny Overcast Rain Rain Rain Overcast Sunny Sunny Rain Sunny Overcast Overcast Rain Temp. Hot Hot Hot Mild Cool Cool Cool Mild Cool Mild Mild Mild Hot Mild Hum. High High High High Normal Normal Normal High Normal Normal Normal High Normal High Wind Weak Strong Weak Weak Weak Strong Strong Weak Weak Weak Strong Strong Weak Strong PlayTennis No No Yes Yes Yes No Yes No Yes Yes Yes Yes Yes No 32. What is the GA and define the necessary genetic operators. 33. What is the classifier system. 35. Using TMS, if a group of people is planning to make a trip, the system is going to make a compromise between all the participants to choose the free day for all. The chosen day has to be warm and either Monday, Tuesday or Wednesday. Show some of the nodes that might be produced by the system in case of contradiction and how the system is going to resolve the problem. (assume some starting nodes) 4