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Aia QB

The document is a question bank for the M.Tech in CSE 2nd Semester course on Artificial Intelligence and Applications, covering a wide range of topics including definitions, algorithms, problem-solving techniques, and applications in AI. Key areas include search algorithms, logic, machine learning paradigms, natural language processing, and expert systems. Each question aims to assess understanding of fundamental concepts and their practical implications in the field of AI.

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Rupesh Kumar
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52 views1 page

Aia QB

The document is a question bank for the M.Tech in CSE 2nd Semester course on Artificial Intelligence and Applications, covering a wide range of topics including definitions, algorithms, problem-solving techniques, and applications in AI. Key areas include search algorithms, logic, machine learning paradigms, natural language processing, and expert systems. Each question aims to assess understanding of fundamental concepts and their practical implications in the field of AI.

Uploaded by

Rupesh Kumar
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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ARTIFICIAL INTELLIGENCE & APPLICATIONS

M.Tech in CSE – 2nd Semester


Session: Jan-June 2025
Question Bank

1. What is Artificial Intelligence?


2. Define state space in problem-solving.
3. What is the difference between blind and informed search?
4. What is alpha-beta pruning?
5. Explain the concept of a game tree.
6. Compare and contrast BFS and DFS with suitable examples.
7. Explain A* and AO* search algorithms with their applications.
8. Discuss the role of heuristic functions in informed search.
9. Describe the Mini-Max algorithm and explain how alpha-beta pruning optimizes it.
10. Explain constraint satisfaction problems and give real-world applications.
11. Define predicate logic and well-formed formula (WFF)?
12. Differentiate between forward and backward chaining.
13. Define semantic networks?
14. Explain the concept of inheritance in semantic networks.
15. What is resolution in theorem proving? Explain rule-based systems with examples.
16. Describe semantic nets, frames, and conceptual dependency formalism.
17. Compare propositional logic and predicate logic with examples.
18. Describe backward reasoning and its use of backtracking.
19. Explain how inference rules are used for knowledge representation.
20. What is uncertainty in AI? Define Bayes' theorem.
21. What is a Bayesian Belief Network (BBN)?
22. Describe learning using neural networks.
23. Mention two limitations of Naïve Bayes.
24. What is fuzzy logic?
25. Differentiate between supervised and unsupervised learning.
26. What is a decision tree? Also explain probabilistic inference using Bayes' theorem.
27. Describe Bayesian Belief Networks and their use in AI.
28. Compare and contrast fuzzy logic with probabilistic logic.
29. Explain various paradigms of machine learning with examples.
30. What is parsing in NLP?
31. Define Machine Translation. What are the components of a planning system?
32. Explain forward planning.
33. What is a planning agent? Discuss key tasks in NLP and their challenges.
34. Describe forward and backward planning with examples.
35. Explain Block World problem and how it is solved using planning.
36. Discuss partial-order planning with a suitable example.
37. Explain State-Goal-Action representation in AI planning systems.
38. What is an expert system? Mention components of an expert system architecture.
39. What is knowledge acquisition? Compare rule-based and non-production systems.
40. Define backtracking in Prolog.
41. Write a simple Prolog fact. Mention differences between LISP and Prolog.
42. Justify the need for expert systems with real-world applications.
43. Explain the architecture of rule-based expert systems.
44. Write Prolog rules for a basic family relationship database.
45. Describe features of LISP language and its application in AI.

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