Q1[3 Marks]Introduction
Differentiate between Human Intelligence and Artificial Intelligence. Discuss the role of Artificial Intelligence in public service delivery in the context of Nepal.
ENCT 305Bachelor in Electronics, Communication and Information Engineering · Semester 53 Papers Available
Artificial Intelligence
Past examination question papers and complete curriculum syllabus for Artificial Intelligence (ENCT 305), Bachelor in Electronics, Communication and Information Engineering Semester 5 under Institute of Engineering (IOE), Tribhuvan University.
Sem 5 Subjects (6)View Full Semester Overview →
ENSH 304Probability and StatisticsENCT 305Artificial IntelligenceENEX 301Filter DesignENEX 302Embedded SystemsENEX 303Propogation and AntennnaENEX 325-344Elective I
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Most Frequently Asked Questions
Top recurring IOE board exam questions for Artificial Intelligence with verified mark schemes, formula notation, and recurrence frequency.
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Year-wise & Session-wise Exam Papers
Complete questions from each exam session (Regular & Back exams from 2082 to 2069) for Artificial Intelligence. Click any session paper to expand.
Showing 307 questions across 24 exam session papers
Institute of Engineering • 2082 Chaitra Board Examination
Q2[2 Marks]Introduction
Define knowledge and learning in the context of AI. Why are they important for intelligent systems?
Q3[3 Marks]Problem Solving and Search
Mention problem formulation of 8-Queen problem with one appropriate solution. Solve the given crypt-arithmetic problem: LOGIC+LOGIC=PROLOG. Show all the required steps.
Q4[3 Marks]Problem Solving and Search
Consider the following game tree for a two-player game.
a) Apply the minimax algorithm and determine the optimal value at the root. Show all intermediate values at each node.
(b) Apply alpha-beta pruning to the same tree. Indicate which branches are pruned and explain why. Show the final alpha and beta values.
Q5[8 Marks]Problem Solving and Search
Compare and Contrast: [2x2]
(a) Informed Vs Uninformed Search Strategies
(b) NLU Vs NLG
Q6[1 Marks]Knowledge Representation and Probabilistic Reasoning
When do we use Fuzzy Logic? Discuss about the different components in Fuzzy Inference System.
Q7[8 Marks]Knowledge Representation and Probabilistic Reasoning
Write short notes on: [2x2]
(a) Fuzzy learning
(b) A search
Q8[2 Marks]Knowledge Representation and Probabilistic Reasoning
Differentiate forward and backward chaining. An individual has 3 different email accounts. Most of his messages, in fact 70% come into account #1, whereas 20% come into account #2 and the remaining 10% into account #3. Of the messages into account #1, only 1% are spam, whereas the corresponding percentages for accounts #2 and #3 are 2% and 5% respectively. What is the probability that a randomly selected message is a spam is from account #2?
Q9[2 Marks]Knowledge Representation and Probabilistic Reasoning
Explain CNF and DNF. Consider the following axioms: Every country that has weapons and is hostile is dangerous. Every country that is enemy of America is hostile. Kadon is a country. Kadon has weapons. Kadon is enemy of America. Any dangerous country should be sanctioned. Using resolution by refutation, prove that Kadon should be sanctioned.
Q10[4 Marks]Knowledge Representation and Probabilistic Reasoning
What is semantic network? Convert the following sentences into semantic network.
i) The avg height of an adult is 5'6".
ii) Cricket player is an adult male.
iii) Adult male is a person.
iv) Batting average of a cricket player is 37.59
v) Rohit Paudel is a Fielder.
vi) Fielder is a cricket player.
vii) Team of Rohit Paudel is Nepal.
Q11[5 Marks]Knowledge Representation and Probabilistic Reasoning
Consider the following knowledge base. Represent them in FOPL and use Resolution refutation technique to prove "Gita likes almond":
- Gita likes almond.
- Gita likes all kinds of food.
- Mango and chapati are food.
- Gita eats almond and is still alive.
- Anything eaten by anyone and is still alive is food.
Q12[2 Marks]Machine Learning Fundamentals
What is reinforcement learning? Explain different steps for reinforcement learning. Give one real world problem where reinforcement learning can be used with proper explanation.
Q13[8 Marks]Machine Learning Fundamentals
During a research work, you encounter a number of dogs that either bark or (try to) bite. The dogs are described by the binary features like Heavy, Smelly, Big and Growling. Consider the following set of examples:
| Heavy | Smelly | Big | Growling | Bites |
|---|---|---|---|---|
| No | No | No | No | No |
| No | No | Yes | No | No |
| Yes | Yes | No | Yes | No |
| Yes | No | No | Yes | Yes |
| No | Yes | Yes | No | Yes |
| No | No | Yes | Yes | Yes |
| No | No | No | Yes | Yes |
| Yes | Yes | No | No | Yes |
Now answer following questions:
(a) What is the entropy of the target value Bites in the data?
(b) Which attribute would the ID3 algorithm choose to use for the root of the tree (without pruning)?
(c) What is the information gain of the attribute you chose in the previous question?
(d) Draw the full decision tree that would be learned for this data using ID3 without pruning.
Q14[6 Marks]Machine Learning Fundamentals
The following table contains test results of 15 sample with real covid cases and a set of predictions from ML algorithm.
| S.no | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Expected | +ve | +ve | -ve | +ve | -ve | -ve | -ve | +ve | +ve | -ve | +ve | +ve | -ve | +ve | +ve |
| Predicted | -ve | +ve | -ve | +ve | +ve | -ve | -ve | +ve | -ve | -ve | +ve | -ve | -ve | +ve | -ve |
a) Compute the confusion matrix for the data.
(b) Compute the accuracy, precision, recall, and F1 score.
Q15[1 Marks]Neural Networks and Deep Learning Algorithms
How best attribute is selected in a decision-tree algorithm? Design a neural network that act as XOR gate.
Q16[2 Marks]Neural Networks and Deep Learning Algorithms
What do you understand by Perception? Discuss about the different activation functions along with their characteristics. Highlight the role of activation function in deep Learning.
Q17[8 Marks]AI Applications
For a Medical Diagnosis Expert System, discuss the methods of knowledge acquisition and draw a detailed block diagram explaining the roles of each component. Also mention the limitations of such expert systems.
Q18[2 Marks]AI Applications
Why Natural Language Processing is hard? List and briefly explain the different levels of linguistic analysis used in NLP. For each level, provide one example of a task performed at that level.
Q19[3 Marks]AI Applications
Explain how computer vision is used in object recognition. A city has deployed a facial recognition system in public areas to enhance security, but citizens are not informed that their faces and movements are being recorded. Identify the privacy concerns raised by this system and suggest one solution to address these concerns.
Q20[8 Marks]Problem Solving and Search
Write short notes on: [3x3]
(a) Embedded AI
(b) Generative Neural Network
(c) Simulated Annealing
Curriculum Syllabus & Course Topics
Sourced from TU curriculum portalChapter-wise Units & Micro-Syllabus Topics (7 Units)
1. Introduction
- 1.1Definition, Foundation, History of AI
- 1.2AI Tree: Branches and Interdisciplinary Nature
- 1.3Definition and importance of Knowledge and Learning
- 1.4Human intelligence and Machine intelligence
- 1.5Intelligent Agents and Types
2. Problem Solving and Search
- 2.1Formal problem definition: States, actions, transitions, well-defined problems
- 2.2Constraint Satisfaction Problems: Node Consistency, Path Consistency, Backtracking
- 2.3Search algorithms, Strategies, and Evaluations
- 2.4Uninformed: BFS, DFS, Iterative Deepening
- 2.5Informed Search: Best First search, Greedy Search, A* algorithm
- 2.6Adversarial search: Minimax algorithm, Alpha-beta pruning
- 2.7Local Search and Optimization: Hill climbing, Simulated Annealing
- 2.8Evolutionary Optimization: Genetic Algorithm
3. Knowledge Representation and Probabilistic Reasoning
- 3.1Knowledge-based Agent
- 3.2Knowledge Representation Techniques and Issues in Representation
- 3.3Propositional & Predicate Logic
- 3.4Semantic networks and frames
- 3.5Probabilistic Reasoning, Inference using Bayes' Theorem
- 3.6Fuzzy logic: membership functions, fuzzy inference systems
4. Machine Learning Fundamentals
- 4.1Forms of Learning: Supervised, Unsupervised, Semi- Supervised, Reinforcement Learning
- 4.2Machine Learning Pipeline
- 4.3Overfitting, Underfitting, Bias- Variance Tradeoff
- 4.4Regression & Classification with Linear Model
- 4.5Classification using Decision Tree & Naive Bayes
- 4.6Evaluation with Confusion Matrix
5. Neural Networks and Deep Learning Algorithms
- 5.1Neural Networks: Structures and Activation Functions
- 5.2Perceptron, Multilayer Perceptron, and Backpropagation
6. AI Applications
- 6.1Expert Systems: Characteristics, Architecture, Development and Various Applications
- 6.2NLP: Level of Analysis and Application
- 6.3Robotics & Computer Vision: Fundamental, Components and Applications
7. AI Ethics and Recent Trends
- 7.1Responsible AI: Bias, Fairness, Explainability
- 7.2Ethical & Societal Considerations
- 7.3Privacy & Security in AI
- 7.4Emerging Trends: Federated Learning, Embedded AI
Examination Scheme & Marks Distribution
Evaluation Structure
- Final Board Theory Exam: 60 Marks (Pass mark: 24)
- Internal Assessment: 40 Marks (Pass mark: 16)
- Practical / Lab Exam: 25 or 50 Marks (Continuous lab evaluation + viva, where applicable)
* This is the general current IOE 60/40 scheme; verify course-specific details in the syllabus above.
Exam Preparation Guidelines
- Review the 3 available past examination papers to identify recurring patterns, core problem types, and chapter weightage.
- Cross-reference key answers with official syllabus units, standard textbooks, and lecture notes.
- Structure answers with labeled diagrams, concise bullet points, and highlight final answers in numerical solutions.
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Curriculum Syllabus & Marking Scheme