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Previous Year Exam Questions of ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEM of BPUT - AIES by Ruparani Mahapatra

  • 2018
  • PYQ
  • Biju Patnaik University of Technology Rourkela Odisha - BPUT
  • Master of Computer Applications
  • MCA
  • 3 Offline Downloads
  • Uploaded 9 months ago
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Registration No : Total Number of Pages : 02 MCA MCC501 5th Semester Back Examination 2018-19 ARTLFICIAL INTELLIGENCE AND EXPERT SYSTEM BRANCH : MCA Time : 3 Hours Max Marks : 70 Q.CODE : E014 Answer Question No.1 which is compulsory and any five from the rest. The figures in the right hand margin indicate marks. Q1 Answer the following questions: Define what do you mean by artificial intelligence Explain about the characteristics of AI problems. What is Turing Test and Why is it performed? What is an Agent? Define Rational Agent. What do you mean by informed search? Define what is SMA* search? List some properties of SMA* search. Show the objects, properties, functions and relations in the following sentence: EVIL KING JOHN, BROTHER OF RICHARD, RULED ENGLAND IN 1200 BC Define syntax and semantics of a first-order logic. Define and explain joint probability distribution. Explain what is meant by belief network? What are the ways in which on can understand the semantics of a belief network? (2 x 10) Explain the technique of simple Hill Climbing algorithm. Differentiate between Hill Climbing and gradient searching techniques. Highlight the limitations of Hill Climbing techniques. Explain the technique of Simulated Annealing and its advantages over Hill Climbing technique. (5) a) Explain the structure of an Agent in details with a neat diagram. (5) b) Compare the algorithms of DFS and BFS with an example, focusing on the (5) a) b) c) d) e) f) g) h) i) j) Q2 a) b) Q3 (5) advantages and disadvantages. Q4 a) Explain the task environment of the WUMPUS WORLD through PEAS (5) description. Q5 Q6 b) Differentiate and explain Forward (progression) and Backward(regression) relevant-states searching techniques with an example. (5) a) Explain what is an Bayesian Network through an example (5) b) Describe the methods for representing the semantics of Bayesian Networks. (5) a) What do you mean by a learning agent? Explain the general model of learning (5) agents. b) Differentiate with an example between Learning by observation and Learning by advice. (5)

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Q7 Consider the following axioms : a) Anyone whom Mary loves is a football star. b) Any student who doesn’t pass, doesn’t play. c) John is a student. d) Any student who doesn’t study doesn’t pass. e) Anyone who doesn’t play is not a football star. (10) Prove by resolution that, if John doesn’t study, then Mary doesn’t love John. Q8 a) Write short answer on any TWO : Agent Environments b) IDA* Search Algorithm c) Planning with Operators d) Simple Inference in Belief Networks (5 x 2)

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