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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.

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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.

Showing 30 of 30 top repeated questions

Introduction

2 Questions
#1Repeated 4 Times[6 Marks]Introduction
Define Artificial Intelligence and describe Intelligent Agents. Formulate the PEAS (Performance measure, Environment, Actuators, Sensors) description and agent environment characteristics for an Automated Autonomous Taxi Driver.
Appeared in:2082 Kartik2080 Ashwin2078 Bhadra2075 Chaitra
#2Repeated 3 Times[8 Marks]Introduction
What is Artificial Intelligence? Explain different types of Intelligent Agents (Simple reflex, Model-based, Goal-based, Utility-based, and Learning agents). Specify the PEAS description for an automated taxi driver agent.
Appeared in:2082 Kartik2080 Chaitra2076 Baisakh

Problem Solving and Search

8 Questions
#1Repeated 5 Times[8 Marks]Problem Solving and Search
What is evolutionary computing? Describe the main features and steps of Genetic Algorithms (Reproduction/Selection, Crossover, Mutation, Fitness function) with an illustrative example.
Appeared in:2080 Chaitra2080 Ashwin2077 Chaitra2074 Bhadra2073 Bhadra
#2Repeated 4 Times[8 Marks]Problem Solving and Search
How A* search overcomes problems associated with Greedy Best First Search? Using path cost $g(n)$ and heuristic evaluation function $h(n)$, explain how A* generates the optimal solution. Explain admissibility and monotonicity conditions.
Appeared in:2082 Kartik2080 Chaitra2077 Chaitra2074 Bhadra
#3Repeated 4 Times[8 Marks]Problem Solving and Search
Explain how Alpha-Beta pruning helps to overcome the limitations of MIN-MAX algorithm in two-player adversarial search. Trace the alpha and beta values on a game tree and identify pruned branches.
Appeared in:2080 Ashwin2076 Baisakh2074 Bhadra2073 Bhadra
#4Repeated 4 Times[8 Marks]Problem Solving and Search
Compare Uninformed Search Strategies: Breadth First Search (BFS), Depth First Search (DFS), Uniform Cost Search (UCS), and Iterative Deepening Search (IDS) in terms of completeness, time complexity, space complexity, and optimality.
Appeared in:2082 Chaitra2081 Ashwin2079 Chaitra2076 Baisakh
#5Repeated 4 Times[8 Marks]Problem Solving and Search
Explain Hill Climbing Search and discuss local extrema problems: Local Maxima, Plateaus, and Ridges. How does Simulated Annealing search escape local optima using the Metropolis probability criterion $P = e^{-\Delta E / T}$?
Appeared in:2082 Kartik2080 Chaitra2077 Magh2074 Bhadra
#6Repeated 4 Times[8 Marks]Problem Solving and Search
Describe the workflow of Genetic Algorithms (GA). Explain Chromosome Encoding, Fitness Function evaluation, Selection mechanisms (Roulette Wheel, Tournament), Crossover operators (Single-point, Two-point), and Mutation probability.
Appeared in:2082 Chaitra2080 Ashwin2077 Chaitra2074 Bhadra
#7Repeated 4 Times[8 Marks]Problem Solving and Search
Explain Constraint Satisfaction Problems (CSP). Formalize the 8-Queens Problem or Map Coloring Problem as a CSP and explain Backtracking search with Forward Checking and the Arc Consistency (AC-3) algorithm.
Appeared in:2082 Kartik2080 Chaitra2078 Bhadra2075 Chaitra
#8Repeated 3 Times[8 Marks]Problem Solving and Search
Discuss the evaluation criteria for search algorithms. State the problems in Hill Climbing search (local maxima, ridges, and plateau) and explain how each can be resolved.
Appeared in:2082 Kartik2076 Bhadra2076 Baisakh

Knowledge Representation and Probabilistic Reasoning

6 Questions
#1Repeated 6 Times[8 Marks]Knowledge Representation and Probabilistic Reasoning
Define Semantic Network and Frames with suitable examples. List down their advantages and limitations in structured knowledge representation.
Appeared in:2082 Kartik2080 Chaitra2080 Ashwin2076 Baisakh2074 Bhadra2073 Bhadra
#2Repeated 5 Times[8 Marks]Knowledge Representation and Probabilistic Reasoning
List down the steps for converting First Order Predicate Logic (FOPL) statements into Conjunctive Normal Form (CNF). Prove a given goal sentence using Resolution Refutation method.
Appeared in:2082 Kartik2080 Chaitra2080 Ashwin2076 Baisakh2074 Bhadra
#3Repeated 4 Times[8 Marks]Knowledge Representation and Probabilistic Reasoning
Explain First-Order Predicate Logic (FOPL) resolution refutation. Convert given English statements into FOPL, convert to Conjunctive Normal Form (CNF / Clausal Form), and prove a theorem using resolution and unification algorithm with substitution $\theta$.
Appeared in:2082 Chaitra2081 Ashwin2080 Chaitra2078 Kartik
#4Repeated 4 Times[6 Marks]Knowledge Representation and Probabilistic Reasoning
Explain Semantic Networks, Frames, and Conceptual Dependencies for structured knowledge representation. Illustrate inheritance hierarchies and default reasoning using suitable domain examples.
Appeared in:2082 Kartik2079 Chaitra2076 Ashwin2073 Bhadra
#5Repeated 4 Times[8 Marks]Knowledge Representation and Probabilistic Reasoning
Explain Bayesian Networks (Belief Networks) and probabilistic reasoning under uncertainty. Given a Conditional Probability Table (CPT) network, compute joint probabilities and posterior beliefs using Bayes' Rule.
Appeared in:2082 Chaitra2081 Ashwin2079 Chaitra2076 Ashwin
#6Repeated 3 Times[8 Marks]Knowledge Representation and Probabilistic Reasoning
Explain probabilistic reasoning in uncertain domains. State Bayes' rule and explain how Bayesian Belief Networks (BBN) represent conditional independence among random variables.
Appeared in:2080 Chaitra2076 Bhadra2074 Bhadra

Machine Learning Fundamentals

6 Questions
#1Repeated 4 Times[6 Marks]Machine Learning Fundamentals
Define Machine Learning. Differentiate between Supervised, Unsupervised, and Reinforcement learning methods with suitable examples.
Appeared in:2082 Kartik2076 Baisakh2074 Bhadra2073 Bhadra
#2Repeated 4 Times[8 Marks]Machine Learning Fundamentals
Explain Multi-Layer Perceptron (MLP) and derive the Backpropagation Algorithm. Compute weight update formulas using gradient descent $\Delta w = -\eta \frac{\partial E}{\partial w}$ with Sigmoid activation function.
Appeared in:2082 Kartik2081 Ashwin2079 Chaitra2076 Ashwin
#3Repeated 4 Times[8 Marks]Machine Learning Fundamentals
What is Reinforcement Learning? Describe the Agent-Environment interaction loop, Markov Decision Process (MDP: $S, A, P, R, \gamma$), the Bellman Equation, and the Q-Learning value iteration update algorithm.
Appeared in:2082 Chaitra2080 Chaitra2078 Kartik2075 Chaitra
#4Repeated 3 Times[8 Marks]Machine Learning Fundamentals
What is the role of activation function in Artificial Neural Networks (ANN)? Explain the McCulloch-Pitts model of a neuron with a diagram. Can a single-layer perceptron be trained to represent an EX-OR gate? Justify.
Appeared in:2080 Chaitra2076 Baisakh2074 Bhadra
#5Repeated 3 Times[8 Marks]Machine Learning Fundamentals
Explain the working principle of a Multi-Layer Perceptron (MLP). Describe the Backpropagation learning algorithm with mathematical derivation for error calculation and weight updates.
Appeared in:2080 Chaitra2074 Bhadra2072 Ashwin
#6Repeated 2 Times[6 Marks]Machine Learning Fundamentals
When do we need a Hopfield Neural Network? Explain its architecture, energy function, and how associative memory retrieval works with an example.
Appeared in:2082 Kartik2076 Bhadra

AI Applications

8 Questions
#1Repeated 5 Times[8 Marks]AI Applications
What is an Expert System? Draw and explain the architecture of an Expert System with a suitable block diagram. Discuss the role of a Knowledge Engineer in developing expert systems.
Appeared in:2082 Kartik2080 Chaitra2080 Ashwin2076 Baisakh2074 Bhadra
#2Repeated 4 Times[8 Marks]AI Applications
What is Natural Language Processing (NLP)? Discuss the different stages involved in NLP (Morphological, Syntactic, Semantic, Pragmatic analysis). Why is NLP difficult?
Appeared in:2082 Kartik2080 Chaitra2076 Bhadra2074 Bhadra
#3Repeated 4 Times[8 Marks]AI Applications
Describe the architecture of an Expert System. Explain Knowledge Acquisition, Inference Engine (Forward Chaining vs Backward Chaining), Rule-Based System, and Conflict Resolution strategies.
Appeared in:2082 Chaitra2081 Ashwin2080 Ashwin2075 Chaitra
#4Repeated 4 Times[8 Marks]AI Applications
Explain Fuzzy Logic and Fuzzy Sets. Contrast crisp sets with fuzzy membership functions $\mu_A(x) \in [0, 1]$. Detail the four steps of a Fuzzy Inference System (Mamdani Model): Fuzzification, Rule Evaluation, Aggregation, and Defuzzification (Centroid Method).
Appeared in:2082 Kartik2080 Chaitra2078 Bhadra2076 Baisakh
#5Repeated 4 Times[8 Marks]AI Applications
Explain Natural Language Processing (NLP) pipeline phases: Morphological analysis, Syntactic parsing (Context-Free Grammars), Semantic analysis, and Pragmatic/Discourse processing. Discuss ambiguity challenges.
Appeared in:2082 Kartik2081 Ashwin2079 Chaitra2076 Baisakh
#6Repeated 4 Times[8 Marks]AI Applications
Explain Edge Detection and Feature Extraction in Machine Vision. Describe the Sobel operator, Prewitt operator, and the multi-stage Canny Edge Detection algorithm (Gaussian smoothing, Gradient magnitude/direction, Non-maximum suppression, Hysteresis thresholding).
Appeared in:2082 Chaitra2080 Ashwin2077 Magh2074 Bhadra
#7Repeated 3 Times[8 Marks]AI Applications
What is Machine Vision? List down and explain the steps involved in the computer vision pipeline (Image acquisition, Pre-processing, Segmentation, Feature extraction, and Object recognition).
Appeared in:2082 Kartik2080 Chaitra2076 Bhadra
#8Repeated 3 Times[8 Marks]AI Applications
What is Fuzzy Logic? Differentiate between crisp set and fuzzy set. Explain the components of a Fuzzy Inference System (Fuzzification, Rule Base, Inference Engine, Defuzzification).
Appeared in:2082 Kartik2080 Ashwin2076 Bhadra

Curriculum Syllabus & Course Topics

Sourced from TU curriculum portal
Full Syllabus (PDF)Micro Syllabus (PDF)
Chapter-wise Units & Micro-Syllabus Topics (7 Units)

  1. 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. 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. 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. 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. 5. Neural Networks and Deep Learning Algorithms

    • 5.1Neural Networks: Structures and Activation Functions
    • 5.2Perceptron, Multilayer Perceptron, and Backpropagation

  6. 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. 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.

Frequently Asked Questions (Artificial Intelligence)

Q: How can I download Artificial Intelligence past question papers?

You can preview or download the Artificial Intelligence question papers (PDF) directly using the built-in viewer on this page with zero redirects or paywalls.

Q: What is the pass mark for Artificial Intelligence?

The general current scheme is a 60-mark final theory exam and a 40-mark internal assessment, with pass marks of 24 and 16. Verify the course-specific syllabus above.

Q: Where can I find the complete syllabus for this subject?

The available chapter-wise syllabus and topic breakdown is indexed in the Syllabus section above, with links to the curriculum PDF source.

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Curriculum Syllabus & Marking Scheme