π Chandigarh University β MCA/BCA/B.Tech
These Mid-Semester papers are part of the official examinations of Chandigarh University,
designed to test knowledge and practical understanding of core MCA, Computer Applications, and B.Tech subjects.
π‘ Each paper encourages students to think critically, apply concepts, and showcase problem-solving skills β helping them prepare for real-world IT challenges.
Usage Condition:
- π Papers are for reference and study purposes only.
- π§βπ Students should use them responsibly and not for any malpractice.
- π Availability depends on the course and year.
Instructions
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This question paper consists of three sections. It is compulsory for students to attempt all questions of Section A and Section C.
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Students need to attempt any one question from question no. 12 and question no. 13 of Section C.
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Students have to attempt any one question from question no. 11 and question no. 10 of Section C.
Artificial Intelligence
Semester: 3 | Time: 3 Hours | Max Marks: 60
| No. | Statement | CO | BT Level |
|---|---|---|---|
| 1 | Mention characteristics of intelligent agents and provide examples of agents in AI. | CO2 | 2 |
| 2 | Examine the characteristics of an effective heuristic function and explain the performance and efficiency of informed search. | CO2 | 2 |
| 3 | Name the advantages of using Hill Climbing over other search methods. | CO2 | 1 |
| 4 | State the condition for termination in the Hill Climbing algorithm. | CO2 | 2 |
| 5 | Analyze the relationship between branching factor and search depth in Minimax trees. | CO3 | 2 |
Section B
| No. | Statement | CO | BT Level |
|---|---|---|---|
| 6 | Apply Depth-First Search to navigate a maze. | CO4 | 3 |
| 7 | Compare the evaluation functions used in Best-First Search and A*. Deconstruct the steps involved in AO* Search. | CO4 | 3 |
| 8 | Describe the concept of local optimum in relation to Hill Climbing. Compare the advantages and disadvantages of Hill Climbing and Simulated Annealing. | CO3 | 4 |
| 9 | Interpret the role of neighbors in the context of Hill Climbing. Identify the meaning of the βgame treeβ in the context of adversarial search. | CO3 | 4 |
Section C-(3*10)
| No. | Statement | CO | BT Level |
|---|---|---|---|
| 10 | Compare Depth-First Search and Iterative Deepening Search. Analyze the efficiency of informed search algorithms. | CO4 | 5 |
| 11 | Critique the efficiency of AO* Search in problem reduction. Assess the impact of admissible heuristics on search algorithms. | CO5 | 4 |
| No. | Statement | CO | BT Level |
|---|---|---|---|
| 12 | Compare neighbor selection in Hill Climbing and Simulated Annealing. Derive the mathematical condition where Simulated Annealing reduces to Hill Climbing. | CO5 | 5 |
