Important RGPV Question

CE 803(A) – Artificial Intelligence

VIII Sem, CE

Unit I – Introduction to Artificial Intelligence

Q.1. Define Artificial Intelligence (AI) and explain its main components.
Q.2. Discuss the role of Feature Engineering in AI systems with examples.
Q.3. What are Artificial Neural Networks (ANN)? Explain their significance in AI.
Q.4. Differentiate between Deep Learning and Machine Learning.
Q.5. List at least five real-life applications of AI in various domains.
Q.6. What are the major advantages of AI over conventional systems?
Q.7. Discuss any three disadvantages or limitations of AI systems.
Q.8. Explain the long-term and short-term goals of AI.
Q.9. Compare programming of a system with AI and without AI.
Q.10. What are the key challenges in implementing AI solutions?
Q.11. Which programming languages are preferred for AI development and why?
Q.12. Explain any two AI techniques/algorithms commonly used in problem-solving.
Q.13. Describe two AI software platforms/tools (e.g., TensorFlow, Keras).
Q.14. What is the future of AI? Discuss possible advancements in the next decade.
Q.15. Write short notes on: (a) Expert Systems, (b) AI Ethics.

Unit II – Production Systems & Search Techniques

Q.16. Define a production system and list its major components.
Q.17. Explain the characteristics of production systems with examples.
Q.18. Discuss the types of production systems used in AI.
Q.19. Differentiate between data-driven and goal-driven control strategies.
Q.20. What is a search problem? Give examples from AI applications.
Q.21. Explain Breadth First Search (BFS) with an example.
Q.22. Compare BFS and Depth First Search (DFS) in terms of efficiency and complexity.
Q.23. What are the limitations of DFS in AI problem solving?
Q.24. Describe Hill Climbing search with its advantages and disadvantages.
Q.25. Explain the Best First Search technique with a suitable example.
Q.26. Write the steps of the A* algorithm and explain its working with an example.
Q.27. What is AO* algorithm? Explain its application in AND-OR graphs.
Q.28. Compare heuristic search and blind search techniques.
Q.29. What are control strategies in search? Describe two examples.
Q.30. Explain the importance of heuristic functions in AI search algorithms.

Unit III – Knowledge Representation & Probabilistic Reasoning

Q.31. What is knowledge representation in AI? Why is it important?
Q.32. List the problems faced in representing knowledge.
Q.33. Explain propositional logic with examples.
Q.34. Differentiate between propositional logic and predicate logic.
Q.35. What is resolution? Explain resolution refutation with an example.
Q.36. Define theorem proving. How is it applied in AI systems?
Q.37. Explain forward and backward reasoning with suitable examples.
Q.38. Differentiate between monotonic and non-monotonic reasoning.
Q.39. What is probabilistic reasoning? Explain with an example.
Q.40. State and explain Bayes’ theorem with a real-life AI application.
Q.41. What are semantic networks? Draw a simple semantic network diagram.
Q.42. Write short notes on: (a) Frames, (b) Scripts.
Q.43. Explain fuzzy logic and its role in AI decision-making.
Q.44. What is conceptual dependency? Explain with a diagram.
Q.45. How does inference work in expert systems? Give one practical example.

Unit IV – Game Playing & Natural Language Processing

Q.46. Explain game playing as an AI problem with an example (e.g., chess).
Q.47. What is the Minimax procedure? How is it applied in AI game playing?
Q.48. Explain the concept of alpha-beta pruning in search trees.
Q.49. Compare Minimax and alpha-beta cut-off techniques.
Q.50. What are the limitations of Minimax in complex games?
Q.51. Explain the concept of adversarial search in game playing.
Q.52. Discuss the block world problem in robotics.
Q.53. How is planning different from searching in AI?
Q.54. Explain the importance of planning in robotics with an example.
Q.55. What is Natural Language Processing (NLP)? List its applications.
Q.56. Describe the challenges in understanding natural languages.
Q.57. Explain parsing and its role in NLP.
Q.58. Differentiate between syntax and semantics in natural language.
Q.59. Discuss any two real-life applications of NLP (e.g., chatbots, translation).
Q.60. What are the major tasks involved in natural language understanding?

Unit V – Learning & Artificial Neural Networks

Q.61. Define machine learning in the context of AI.
Q.62. Explain supervised, unsupervised, and reinforcement learning with examples.
Q.63. What is an Artificial Neural Network (ANN)?
Q.64. Draw and explain the architecture of a simple three-layer ANN.
Q.65. What is the function of neurons and activation functions in ANN?
Q.66. Explain Convolutional Neural Networks (CNN) and their applications.
Q.67. Differentiate between Feedforward and Recurrent Neural Networks (RNN).
Q.68. What is a Multilayer Perceptron (MLP)? Explain its working.
Q.69. Describe the steps involved in implementing an ANN.
Q.70. Explain backpropagation algorithm used in neural network training.
Q.71. What are the applications of ANN in image recognition and signal processing?
Q.72. How are neural networks applied in natural language tasks?
Q.73. Explain the concept of “common sense reasoning” in AI systems.
Q.74. What are expert systems? Give examples of AI expert systems.
Q.75. Discuss the advantages and limitations of neural networks.

— Best of Luck for Exam —