Tribhuvan University
Bachelor of Science in Computer Science and Information Technology
Semester 4 · TU Board 2082
Course Title: Artificial Intelligence (CSC266)
Full Marks: 60Pass Marks: 24Time: 3 hours
Candidates are required to give their answers in their own words as far as practicable. The figures in the margin indicate full marks.
Group A
Attempt any TWO(2 × 10 = 20)
- 1.10
What are the problems of depth limited search? Use the Alpha-Beta pruning algorithm to prune the given game tree, assuming child nodes are visited left to right. Show all final alpha and beta values computed at root, each internal node explored, and at the top of pruned branches.
[figure in the original paper]
- 2.10
How do you infer knowledge from a semantic net? Illustrate with an example. Consider the following Bayesian network with random variables A, B, C and D. If A is true, find the probability of D being true.
[figure in the original paper]
- 3.10
What is model free reinforcement learning? Differentiate between active and passive reinforcement learning. Describe the different operators used in genetic algorithm.
Group B
Attempt any EIGHT(8 × 5 = 40)
- 4.5
How do you define rational? Can AI choose between right and wrong? Justify.
- 5.5
What do you mean by PEAS description? Give an example.
- 6.5
Apply the hill climbing search algorithm to solve the following problem. Assume the heuristic function h(n) = +1 for all blocks in the support structure if the block is correctly positioned, otherwise -1.
Initial State:
Goal State:
- 7.5
Why do we need posterior probability? Design a semantic network for the following statements: Dogs hate cats. Tom is a cat. Puppy is a dog. Cats chase rats. Rats are clever.
- 8.5
Design a Hebb net to implement the logical OR function.
- 9.5
Given the premises: All living things are either animal or plant. Plants need sunlight for photosynthesis. Mustard is a living thing but it is not an animal. Does mustard need sunlight for photosynthesis?
- 10.5
What are the phases for developing an expert system? Explain.
- 11.5
Why do machines need to understand and generate natural language? What might be the challenges? Explain.
- 12.5
List any one example of static and dynamic environment for an agent. Why and when do you prefer rule based systems?
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