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2003 Course Artificial Intelligence

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Total No. of Questions : 12] P1631 [Total No. of Pages : 2 [3764]-254 B.E. (Electronics) ARTIFICIAL INTELLIGENCE (2003 Course) Time : 3 Hours] [Max. Marks : 100 Instructions to the candidates : 1) Answer 3 questions from Section I and 3 questions from Section II. 2) Answers to the two sections should be written in separate books. 3) Use of logarithmic tables slide rule, Mollier charts, electronic pocket calculator and steam tables is allowed. 4) Assume suitable data, if necessary. SECTION - I Q1) a) Explain simulated Annealing algorithm in detail. Also explain how to select an annealing schedule. [10] b) Explain any two applications of Artificial Intelligence in detail. OR [8] Q2) a) Explain the algorithm in detail which combines the advantages of both [10] depth-first and breadth-first search into a single method. b) Explain Simple Hill climbing algorithm. [8] Q3) a) Explain resolution in Predicate logic with examples. [8] b) Explain how simple facts are represented in logic. What are different ways to represent a class membership. [8] OR Q4) a) Explain minimax search procedure. How the performance of the minimax procedure can be further improved? [8] b) Explain the need of Natural deduction in detail. [8] Q5) a) Explain in detail Non monotonic Reasoning. b) Explain statistical Reasoning & Fuzzy logic briefly. [8] [8] OR P.T.O. Q6) a) Address the implementation issues of non monotonic reasoning. [8] b) Explain how frames can be used for knowledge representation. Give an example. [8] SECTION - II Q7) a) Explain Nonlinear planning in detail. b) Explain Hierarchical planning in detail. [8] [8] OR Q8) a) What is Planning System. Describe the different components of planning system. [8] b) Explain Goal Stack Planning in detail. Q9) a) Explain Waltz Algorithm in detail. [8] [9] b) Explain the need of multilayer networks. Also explain the applications of the same in detail. [9] OR Q10) a) Explain Back propagation algorithm in detail. Also explain the term batch processing with training of a neural network. [9] b) Describe in detail various training methods used in Neural Networks [9] with examples. Q11) a) Explain Syntactic & Semantic analysis in short. [10] b) Define expert system and give an approach to built an expert system.[6] OR Q12) a) Explain how ATN is used for natural language understanding. b) Write a note on Finite State Machine. rrrr [3764]-254 -2- [10] [6]

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