B.Sc. (Computer Science) Sem 5 Artificial Intelligence Oct 2025 Question Paper - Mumbai University | munotes

Questions asked in this paper

• N.B. (1) All questions are compulsory. (2) Figures to the right indicate marks. (3) Illustrations, in-depth answers and diagrams will be appreciated. (4) Mixing of sub-questions is not allowed.

SECTION I - Q.1 (Attempt ANY FOUR from the following) 20 Marks

1. (a) Write States, Initial States, Actions, Transition Model, Goal Test and Path Cost for 8-puzzle problem.
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2. (b) How do Mathematics and Psychology serve as foundational pillars of Artificial Intelligence?
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3. (c) Write short note on Simple Reflex Agents.
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4. (d) Explain Iterative Deepening Depth First Search strategy with suitable example.
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5. (e) What do you mean by PEAS? Mention it for Medical diagnosis system and Part Picking Robot.
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6. (f) Consider the below search problem along with heuristic values. [Graph: A (Start) to C cost 118, A to E cost 140, A to B cost 75; C to D cost 111; E to G cost 80, E to F cost 99; G to H cost 97; H to I (Goal) cost 101; F to I cost 211. Heuristic h(n): A 366, B 374, C 329, D 244, E 253, F 178, G 193, H 98, I 0. f(n) = h(n) = straight-line distance heuristic.] Find the path using Greedy Best First Search Algorithm.
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SECTION II - Q.2 (Attempt ANY FOUR from the following) 20 Marks

1. (a) Explain the architecture of Fuzzy Logic System.
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2. (b) Explain the concept of Polynomial Regression.
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3. (c) What is a Backpropagation Neural Network and how does it work?
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4. (d) What do you mean by Knowledge Representation? Explain any two types of Knowledge.
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5. (e) Explain the concept of Decision Tree. Build a decision tree for the restaurant waiting problem.
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6. (f) What is overfitting and underfitting in machine learning? How do they affect model performance?
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SECTION III - Q.3 (Attempt ANY FOUR from the following) 20 Marks

1. (a) Explain Bayesian Learning with an example.
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2. (b) Write a short note on Passive Reinforcement Learning.
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3. (c) Explain Q- Learning in detail.
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4. (d) Explain the concept of Unsupervised Learning.
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5. (e) Explain Maximum-likelihood parameter learning for Continuous models.
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6. (f) Consider the database given below. [T1: Idli, Dosa, Vada, Payasam; T2: Idli, Pongal, Dosa, Filter Coffee; T3: Idli, Filter Coffee, Kesari; T4: Filter Coffee, Pongal, Kesari; T5: Kesari, Dosa, Payasam, Filter Coffee; T6: Pongal, Filter Coffee, Idli.] Calculate: (i) Support (Dosa) (ii) Support (Idli, Dosa) (iii) Confidence (Filter Coffee => Kesari) (iv) Confidence (Idli => Pongal) (v) Lift (Dosa, Payasam)
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SECTION IV - Q.4 (Attempt ANY FIVE from the following) 15 Marks

1. (a) Define heuristic function. Give an example heuristic function.
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2. (b) Describe problem formulation of vacuum world problem.
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3. (c) Write note on (i) Universal Quantifier (ii) Existential Quantifier
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4. (d) What is an Artificial Neural Network?
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5. (e) Write in short about hidden and Latent Variables.
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6. (f) Write a note on Naive Bayes models.
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