JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech II Year INTRODUCTION TO ARTIFICIAL INTELLIGENCE Previous Question Paper (February - 2024 - R22)
Note: This question paper contains two parts A and B. Part- A for 10 marks, Part - B for 50 marks. Part-A is a compulsory question. Part-B consists of ten questions (numbered from 2 to 11) carrying 10 marks each. From each unit, there are two questions and the student should answer one of them. Hence, the student should answer five questions from Part-B.
Part A
Differentiate between acting humanly and acting rationally.
Answer
Acting humanly focuses on mimicking human behavior, including cognitive processes and potential errors, whereas acting rationally focuses on achieving the best possible outcome or goal based on available information, regardless of whether the process mimics human behavior.
What does it mean to pass the Turing test?
Answer
Passing the Turing test means an AI system exhibits intelligent behavior equivalent to, or indistinguishable from, that of a human, such that a human evaluator cannot reliably tell the machine from a human through text-based conversation.
What is a heuristic function in the context of heuristic search?
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Differentiate between solution space search and state space search.
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Write the advantages of randomness in search algorithms.
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Discuss the limitations of using a randomized search for TSP.
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Why is brute force often impractical for large search spaces?
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Define the term admissibility in the context of the A* algorithm.
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Give one example scenario where SAINT could be effectively applied.
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What is a goal tree?
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Part B
Explain the role of philosophy, mathematics, economics and neuroscience in the foundation of artificial intelligence.
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Explain the breadth-first search algorithm in the context of state space search.
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How does the choice of search algorithm impact the quality of the solution obtained? Explain.
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Explain the major milestones and breakthroughs in AI research over the years.
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Discuss the exploration-exploitation trade-off in hill climbing algorithms.
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Discuss the trade-offs involved in choosing the beam width in beam search.
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Describe the variable neighborhood descent algorithm and its key components.
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How do peak-to-peak methods address challenges like convergence and exploration? Explain.
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Explain the iterated hill climbing algorithm. How does it address local maxima issues?
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How can randomness be incorporated into traditional search algorithms to improve their efficiency? Explain.
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How does the genetic algorithm maintain population diversity during search? Explain with an example.
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Discuss the challenges and opportunities associated with studying and modeling emergent systems.
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What is the brute force approach for finding optimal paths, and what are its limitations? Explain.
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Explain the A* algorithm with a suitable example.
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Describe Dijkstra’s algorithm and its application in finding the shortest path in a graph. State its time and space complexity.
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Discuss the concept of divide and conquer beam stack search. How does it break down the problem of finding optimal paths?
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Discuss the significance of Dendral in the development of expert systems.
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How does XCON utilize rules and knowledge to configure complex systems? Explain.
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How can SAINT, goal trees, and rule-based Systems be integrated for effective problem decomposition in an expert system? Explain.
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Discuss potential future trends in problem decomposition techniques for expert systems.
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