# Midterm Examination CS540: Introduction to Artificial Intelligence

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1 Midterm Examination CS540: Introduction to Artificial Intelligence October 27, 2010 LAST NAME: SOLUTION FIRST NAME: SECTION (1=Zhu, 2=Dyer): Problem Score Max Score Total 100 1

2 Question 1. [15] State Space Search You have two jugs, measuring 3 gallons and 4 gallons, and a water faucet. You can fill the jugs up or empty them out from one to another or onto the ground. You need to measure out exactly 2 gallons. (a) [5] Let state (a, b) mean the amount of water in the 3 and 4 gallon jugs, respectively. Define all successors of this state. (3,b); if a<3 (a,4); if b<4 (0,b); if a>0 (a,0); if b>0 (3, b-(3-a)=a+b-3); if a<3 and a+b>3 (a+b, 0); if a<3 and b>0 and a+b<=3 The two above can be combined into (min(a+b,3), max(a+b-3,0)) (a-(4-b)=a+b-4, 4); if b<4 and a+b>4 (0, a+b); if b<4 and a>0 and a+b<=4 The two above can be combined into (max(a+b-4,0), min(a+b,4)) (b) [5] Write down a solution path from (3, 4) to a goal state. You do not need to show how you find this path. (3,4)-(3,0)-(0,3)-(3,3)-(2,4) or (3,4)-(0,4)-(3,1)-(0,1)-(1,0)-(1,4)-(3,2) (c) [5] Let the cost of every edge be 1. Consider the function h(a, b) = a - b. Is h an admissible heuristic? Justify your answer. No. On many goal states this h is not zero. On the first solution path, (0,3) has actual cost 2 (i.e. two steps away from goal) but h(0,3)=3>2. Similarly, on the 2 nd solution path, (1,4) has actual cost 1 but h(1,4)=3>1. 2

3 Question 2. [16] Uninformed and Informed Search Consider the following path-finding problem. One can move from one small triangle to another if they share a vertex (e.g., A can go to B and C). However, the goal G can only be accessed from F. The number after the letter is the heuristic function value for that state. The actual cost of each move is as follows: A move down one level (e.g. A C or B E) costs 1 A move sideways on the same level (e.g. C B or E F) costs 2 A move up one level (e.g. B A or C A ) costs 3 (a) [8] Perform Depth-First Search, starting from A, using path-checking to avoid repeated states if they occur on the path back to the root in the search tree. Expand successors in alphabetical order. Show your search tree, and circle states that are expanded. What is the cost of your solution path? A B C C D E F D E F E F H F H G H Cost=11 3

4 (b) [8] Perform A* Search, starting from A. Break ties alphabetically. Show the expanded states and the priority queue contents at each step. What is the cost of your solution path? Expanded states priority queue - A(0+2) A(0+2) B(1+2), C(1+1) C(1+1) B(1+2), D(2+3), E(2+2), F(2+1) B(1+2) D(2+3), E(2+2), F(2+1) F(2+1) D(2+3), E(2+2), G(5+0), H(3+3) E(2+2) D(2+3), G(5+0), H(3+3) D(2+3) G(5+0), H(3+3) G(5+0) H(3+3) Path: A-C-F-G, cost=5 4

5 Question 3. [7] Simulated Annealing Assume that simulated annealing starts from a state s at the center of a large plateau. That is, the values of all states on the plateau are exactly the same. (a) [4] Say in the first step the random neighbor we picked is t, which of course has the same value as s. What will simulated annealing do? Justify your answer. Since P(s->t) = exp(- f(s)-f(t) /T) and f(s)=f(t), we see that with probability 1 we will move to t. (b) [3] To encourage the search to escape this plateau as quickly as possible, what should we do with the temperature T? Justify your answer. Since the above probability P(s->t) does not depend on T (because it is exp(0)), there is nothing you can do about temperature T to encourage an escape. 5

6 Question 4. [15] Game Playing Consider the following game where A moves first. The two players take turns. Each player must move to an open adjacent space in either direction. If the opponent occupies an adjacent space, then a player may jump over the opponent to the next open space, if there is one (for example, if A is on 1 and B is on 2, then A may jump to 3). The game ends when A reaches 4 (the value of this game to A is +1), or B reaches 1 (the value of this game to A is -1). (a) [8] Draw the complete game tree, using the following conventions: Write each state as (a, b) where a and b denote the locations of A and B, respectively. Put each terminal state in a square box, and write its game value below the box. Put loop states (i.e., states that already appear on the path back to the root) in double square boxes. Since it is not clear how to assign values to loop states, write a? below these double boxes. (1, 4)? (1, 4) (2, 4) (2, 3) +1 (1, 3) (4, 3) +1 (1, 2) (3, 2) (3, 4) (3, 1) -1 (2, 4)? (b) [4] Compute the minimax value at every internal node, showing them in the search tree as well. Explain how you handled the? values and why. See red numbers above. The? value can be obtained by copying the value of the (2,4) node next to the root. Or: it doesn t matter because B will select the right branch at (3,2). 6

7 (c) [3] Explain why the standard minimax algorithm would fail on this game tree. The game tree is infinite. Because minimax is depth first search, it will not terminate. Question 5. [12] Constraint Satisfaction Consider the problem of assigning colors to the five squares on board below such that horizontally adjacent and vertically adjacent squares do not have the same color. Assume there are possible two colors, red (R) and black (B). Formulated as a constraint satisfaction problem, there are five variables (the squares) and two possible values (R, B) for each variable. (a) [3] If initially every variable has both possible values and we then assign variable 1 to have value R, what is the result of the Forward Checking algorithm? Forward checking propagates constraints from the current assigned variable to all adjacent unassigned variables, which in this case are variables 2 and 4. This results in the following domains for the variables: 1 = {R}, 2 = {B}, 3 = {R, B}, 4 = {B}, 5 = {R, B} (b) [4] If initially every variable has both possible values and the Arc Consistency algorithm is run, what are the resulting domains for each of the variables? None of the domains change, with each variable still have both possible values, {R, B}. (c) [5] If initially every variable has both possible values except variable 5 has only value B, what is the result of the Arc Consistency algorithm? 1 = {B}, 2 = {R}, 3 = {B}, 4 = {R}, 5 = {B} 7

8 Question 6. [10] Propositional Logic (a) [2] True or false: (A B) = ( A B) True (b) [4] Is the PL sentence ((P Q) Q) P valid, unsatisfiable or satisfiable? Justify your answer. Satisfiable. When P=T and Q=T the sentence is true, but when P=F and Q=T the sentence is false. (c) [4] Given two arbitrary sentences and in PL, = if and only if i. [2] the sentence is valid (one word answer) ii. [2] the sentence is unsatisfiable (one word answer) 8

9 Question 7. [15] Deductive Inference in Propositional Logic Consider the following KB containing 4 sentences in PL: P (R S), P (R S), S, (R U) Q (a) [3] Can these 4 sentences be expressed as a set of Horn clauses? If yes, give them; if no, explain why not. No because the first sentence has two literals on the right hand side, meaning that the CNF has two positive literals in it. (b) [4] Convert the given sentences into conjunctive normal form (CNF) and show the result as a set of clauses. P R S, P R S, S, R Q, U Q (c) [8] Prove the query sentence Q is entailed by KB using the Resolution algorithm. 1. Q negation of query 2. P R S KB 3. P R S KB 4. S KB 5. R Q KB 6. U Q KB 7. R RR(1,5) 8. P S RR(2,7) 9. P RR(4,8) 10. R S RR(3,9) 11. R RR(4,10) 12. False RR(7,11) 9

10 Question 8. [10] Representation in First-Order Logic For each of the following sentences in English, is the accompanying sentence in first-order logic a good translation? If yes, answer yes. If no, explain why not. (a) [5] No two UW students have the same ID number. p,q,n (UWStudent(p) UWStudent(q) (p = q)) (IDNum(p, n) IDNum(q, n)) This is NOT correct because it uses instead of when quantified by. It should be: p,q,n UWStudent(p) UWStudent(q) (p = q) IDNum(p, n) IDNum(q, n) (b) [5] All UW students except math majors like CS majors. p,q (UWStudent(p) MathMajor(p)) (UWStudent(q) CSMajor(q) likes(p, q)) This is NOT correct because it says that if there is at least one UW student who is not a math major, then everyone has to be a UW CS major. A corrected version is: p,q (UWStudent(p) MathMajor(p) UWStudent(q) CSMajor(q)) likes(p, q) 10

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