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AI Practice Questions IA2

This document contains practice questions for the Internal Assessment-2 of the Artificial Intelligence course at Shah & Anchor Kutchhi Engineering College for the academic year 2023-2024. The questions cover various AI topics including predicate logic, chaining methods, genetic algorithms, reinforcement learning, and planning techniques. Students are required to explain concepts, compare methods, and provide examples for a comprehensive understanding of the subject.

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17 views3 pages

AI Practice Questions IA2

This document contains practice questions for the Internal Assessment-2 of the Artificial Intelligence course at Shah & Anchor Kutchhi Engineering College for the academic year 2023-2024. The questions cover various AI topics including predicate logic, chaining methods, genetic algorithms, reinforcement learning, and planning techniques. Students are required to explain concepts, compare methods, and provide examples for a comprehensive understanding of the subject.

Uploaded by

rjcsahil12sci30
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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explani

Mahavir Education Trust's


SHAH & ANCHOR KUTCHHI ENGINEERING COLLEGE,

Chembur, Mumbai - 400 088

UG Program in Computer Engineering

Subject: Artificial Intelligence (AI)

Practice Questions for Internal Assessment-2

Year: 2023-2024 Class: TE3, TE4 & TE9

Semester: VI

Q1. Write short note on

i) Predicate Logic

ii) Properties of the environment

Q2. Compare forward and backward chaining.

Q3. Explain backward chaining giving suitable example.

Q4. Explain forward chaining giving suitable example.

Q5. Give the drawbacks of Hill Climbing Algorithm.

Q6. What is bayes theorem. Draw and explain Bayesian belief network and make Inference
using Bayesian belief network.

Q7. Explain various FOL inference rules with an example.

Q8. What is optimization. Draw and explain the flow chart of genetic algorithm.

Q9. Explain roulette wheel with an example.

Q10. Explain tournament selection with an example.


Q11. Define crossover. Explain types of crossover with example

Q12. Define mutation. Explain types of mutation techniques with example.

Q13.What are stopping criteria for genetic algorithm.

Q14. Define Landscape. Draw landscape and explain it in detail.

Q15. Explain hill climbing with respect to 8 queens problem.

Q16. What is adversarial search algorithms.

Q17. Examples of max min search.

Q18. Apply alpha beta pruning to any given example.

Q19. Explain partial order planning with example(Spare tire problem, Shoe-sock problem).

Q20. Compare hierarchical planning and partial order planning.

Q21. Define reinforcement learning. What are rewards and penalties in reinforcement
learning.

Q22.Short note on AI applications in Healthcare or Retail or Banking.

Q23. Explain active reinforcement learning in detail.

Q24. Explain passive reinforcement learning in detail.

Q25. numericals/examples of propositional logic, first-order logic, belief network.

Q26. Explain A* algorithm with example.

Q27. What is heuristic function.? Give heuristic function for a given problem( 8 puzzle, 8
queens ).

Q28. Consider the following statements:

a) All people who are earning are happy

b) All happy people smile

c) Someone is earning
d) There is a white cat

Represent the above statements in FOL.

Q29. Explain quantifiers with example.

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