Computer Science 188 - Fall 2000 - Wilensky - Final Exam

# Computer Science - CS188 Intro to AI Fall 2000 R Wilensky Final Examination This is an open-book open-notes exam Write your name etc in the space

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CS188 Intro. to AI Fall, 2000 R. Wilensky Final Examination This is an open-book, open-notes exam. Write your name, etc., in the space below; answer all questions in the space provided. (Space is provided for your name on the top of each page as well.) You have 3 hours to work on the exam. There are 125 points total. Questions vary in difficulty; do what you know first . Good luck! NAME: SID: TA: ( Space below for official use only. ) Problem 1: (20) Problem 2: (25) Problem 3: (20) Problem 4: (30) Problem 5: (20) Problem 6: (10) Total: (125)

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Name, SID: 2 Problem 1 (20 points, 2 points each) For each statement below, say if it is true or false, and give a one sentence explanation of your answer. (a) The sentence “ 2200 x,y Parent ( x , y ) Child ( y , x ) ” is satisfiable but not logically valid. True: True if Parent and Child are mapped to inverse relations, which of course, they may not be. (b) Any linearly separable data set can be learned by some single layer perceptron. True: We proved that perceptrons learn exactly the linearly separable sets. (c) Decision tree learning algorithms may be subject to overtraining, but not neural network learning algorithms. False: We meant overfitting , which can (and does) occur in NNs as well. (The typo didn ’t seem to bother many of you, and overtraining is not a bad term for what happens anyway.) (d) Given the expression (which we corrected during the exam, to include r as an existential variable to and make the ! a 1) 5 g,a,r,p,d Ind ( g,Giving ) Agent ( a,g ) Recipient ( r,g ) Donor ( d,g ) Theme ( p,g ) we can derive the following expression, assuming that all the constants do not otherwise appear in any other formula: Ind(G1,Giving) Agent(A1,G1) Recipient (R1,G1) Donor (D1,G1 ) Theme ( P1,G1 ) True: Via Existential instantiation (Skolemization) (e) Temporal difference learning can be used for deterministic MDPs, but not for non-deterministic tasks.
Name, SID: 3 False: TD-Gammon is a case in point. (f) If a heuristic always returns the same value no matter what the state, it cannot be admissible. False. It can still be admissible, just not terribly useful. (g) The Markov assumption enables the Viterbi algorithm to be computationally tractable. True. It allows us to limit the amount of state we need to keep track of. (h) A set of propositions in a “production system ”, interpreted using a set of conflict-resolution strategies, has the same semantics as they would if interpreted as a knowledge base of logic formulas. False. In logic, a sentence like “A x P(x)” means that P is true for all x; in a production system, it might mean that P is true for all x except where there is more specific information. (i) Back-propagation is equivalent to using gradient descent to find a local minimum of an error function

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## This note was uploaded on 05/17/2009 for the course CS 188 taught by Professor Staff during the Spring '08 term at University of California, Berkeley.

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Computer Science - CS188 Intro to AI Fall 2000 R Wilensky Final Examination This is an open-book open-notes exam Write your name etc in the space

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