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Friday, August 14, 2026

Artificial Intelligence Unit-wise Question Bank | Subject Code: BCS701

Artificial Intelligence (BCS701)

Unit-wise Question Bank & Previous Year Questions (AKTU)

Unit 1

Introduction to Artificial Intelligence & Intelligent Agents

Explain the term "Artificial Intelligence" (AI). What are the major characteristics of AI?
How is Artificial Intelligence different from natural (human) intelligence?
Discuss the historical background, foundation, and evolution of Artificial Intelligence.
What is the state-of-the-art of Artificial Intelligence? Write a short note on its current capabilities.
Define the Turing Test. Is the Turing Test sufficient to define the operational definition of AI?
List the six disciplines needed for an Intelligent System to pass the Turing Test.
What do you mean by an agent program? How do you assure that an agent program is an intelligent agent program?
Define an Intelligent Agent. Explain the architecture and main components of intelligent agents with a suitable diagram.
Differentiate between a standard agent and a rational agent. Describe the role of a rational agent.
Describe the basic types of agent programs (e.g., reactive agents, deliberative agents, learning agents).
Differentiate between proactive and reactive intelligent agents. Provide applications for each.
What are the specific advantages of using various communicating agents?
Define the four categories of Intelligent Systems.
Explain PEAS (Performance, Environment, Actuators, Sensors) and the properties of task environments.
Design the PEAS description for a "Satellite Agent", an "Automated Car Driving System", and an "Online Shopping Agent".
How are task environments and rational agents related?
Explain the role of sensors and effectors (actuators) in the functioning of intelligent agents.
Discuss the consideration of temporal aspects and decision-making modules in the architecture of intelligent agents.
What is problem space? Explain the state space approach for solving any AI problem.
Why must problem formulation follow goal formulation?
Explain problem-solving methods in AI and how they differ from traditional computational approaches.
Briefly describe the meaning of knowledge representation and knowledge acquisition. What procedure is followed for knowledge acquisition?
Describe Semantic Nets and Frames with a suitable example. Draw a hierarchical network to represent it.
Explain Transition Networks with the help of a suitable example.
Explain the term "Computational Intelligence" in brief.
What are the key differences between Artificial Intelligence, Machine Learning, and Deep Learning?
Discuss various real-world applications of AI and its impact on everyday life and different industries.
Explain the contribution of intelligent agents in Artificial Intelligence.
Describe the role of Computer Vision in Artificial Intelligence.
Why is game playing a good candidate for AI? Explain.
What are Software Agents? Distinguish intelligent software agents from intelligent agents in AI.
Discuss the impact of quantum computing on the future development and capabilities of intelligent agents.
Explain the concept of default reasoning in Artificial Intelligence.
What is meant by the ethical approach in AI? Discuss its importance.
How do intelligent agents perceive and act within their environment in the context of multi-agent systems?
What are the key characteristics that define an intelligent agent in a multi-agent system?

Unit 2

Problem Solving & Search Strategies

What are the different parameters used to evaluate a search technique (e.g., Completeness, Space Complexity, Optimality)?
Differentiate between uninformed search and informed (heuristic) search strategies with examples.
Define Breadth-First Search (BFS) and Depth-First Search (DFS). Explain them with suitable examples.
Derive the expressions for time and space complexity of BFS and DFS strategies.
Show that Depth-First Search is neither complete nor optimal.
Prove that Breadth-First Search and Depth-First Search are special cases of Best-First Search.
Prove that Breadth-First Search is a special case of Uniform Cost Search.
Compare Depth-Limited Search and Iterative Deepening Depth-First Search on the basis of problem-solving performance parameters.
Describe the procedure for Iterative Deepening Breadth-First Search.
Explain the Uniform-Cost Search algorithm.
What is a heuristic function? State the significance of using heuristic functions in AI.
Why is heuristic search better than blind search? Give the desirable properties of a heuristic search algorithm.
Define Heuristic Search. Explain the Greedy Best-First Search algorithm with an example.
Describe the A* search technique. Prove that A* is complete and optimal. Justify with an example.
Explain the AO* algorithm with a suitable example.
What is Local Search? Differentiate between local search and global search.
Explain the Hill Climbing algorithm (including Steepest-Ascent) with a suitable block diagram.
What are the common problems and limitations associated with the Hill Climbing algorithm? When will it fail?
What do you mean by local maxima and local minima with respect to search techniques?
Discuss Branch-and-Bound search algorithms. How can this technique be used to find the shortest path solution to a Traveling Salesman Problem?
Write a short note on the Iterative Improvement Algorithm.
Define Constraint Satisfaction Problems (CSP). Explain the constraint satisfaction procedure.
Solve the following Cryptarithmetic problems using CSP:
- SEND + MORE = MONEY
- CROSS + ROADS = DANGER
- BASE + BALL = GAMES
Define the Water Jug problem in AI. Discuss the problem with heuristic search techniques and suggest a complete state space solution.
Describe the N-Queens problem in detail. How do you solve the 8-Queen problem using State Space Search?
Solve the 8-puzzle problem with the help of the A* Algorithm.
Define Backtracking Search. Discuss the importance of a well-defined problem representation in the success of backtracking algorithms.
What is Adversarial Search? Write the steps for game problem formulation.
Give an example of a game tree. What is the purpose of the Min-Max procedure in a game tree?
State and explain the Min-Max algorithm using the Tic-Tac-Toe game.
What is Alpha-Beta Pruning? How does it improve the Min-Max algorithm? Evaluate a given problem using alpha-beta pruning.
Explain stochastic games and partially observable games. What challenges arise when dealing with partial observations?
Discuss how Nash equilibrium and cooperative game theory concepts apply to negotiation scenarios.

Unit 3

Knowledge Representation & Reasoning

What are the desirable properties of good knowledge representation schemes? List the various schemes available.
Differentiate between Knowledge Representation and Knowledge Acquisition.
Discuss the major approaches to knowledge representation in detail.
What is the difference between Propositional Logic and Predicate Logic?
Define a well-formed formula (WFF) and list the rules of inference.
What is Propositional Theorem proving? Explain the various concepts used with the help of an example.
Explain the syntax and semantics of First-Order Predicate Logic (FOPL) and how it is utilized in Prolog programming.
What is Modus Ponens rule in Propositional Logic?
What are the limitations of using propositional logic to represent a knowledge base?
Define Forward Chaining and Backward Chaining. Differentiate between them with an example.
Under what conditions would it make sense to use both forward and backward chaining?
What is Unification in the context of logic programming? Explain the unification algorithm used for reasoning under predicate logic.
What is Resolution? Discuss the process and role of resolution in logic programming/predicate logic.
Explain the concept of Skolemization and Existential Generalization in the context of first-order logic.
Convert a given sentence into predicate logic and then prove it using resolution (e.g., "Marcus is Dead").
Represent the following sentences in predicate logic:
- "Everyone is loyal to someone."
- "All the children like sweets."
Explain knowledge-based agents using the Wumpus World environment.
What is Ontological Engineering? Discuss its importance and explain reasoning using ontologies.
Apply the concepts of upper ontology with an example. Explain the categories and objects in ontological engineering.
Compare Semantic Networks and Frames as knowledge representation methods.
What is Meta-Knowledge?
Write down the main features of the PROLOG language. Write a basic PROLOG program that creates a knowledge base.
Write down the various data types in the LISP language and their syntax.
What are Planning Graphs? Explain the methods of planning and acting in the real world.
Explain the importance of consistency in logical systems and state the soundness property of inference.

Unit 4

Uncertainty & Learning Techniques

Explain the need for probabilistic reasoning in Artificial Intelligence.
What is Probability Theory, and how is Bayes' Theorem associated with it? Derive and explain Bayes’ Rule with an example.
Describe Bayesian Networks. How are Bayesian networks a powerful representation for uncertainty knowledge?
Explain the method of handling approximate inference in Bayesian Networks.
Explain the concept of Maximum a Posteriori (MAP).
What is the role of a Hidden Markov Model (HMM) in probabilistic reasoning?
What is Machine Learning? Discuss its application areas and various domains.
Differentiate between Supervised, Unsupervised, and Reinforcement Learning with appropriate examples.
What is a Learning Agent? Explain the characteristics, applications, and components needed to build a learning agent with the help of a sketch.
Describe Statistical Learning Models in detail. Write short notes on discrete models (maximum-likelihood parameter learning) and continuous models.
Explain learning with complete data (Naïve Bayes Model) and learning with hidden data (Expectation-Maximization / EM algorithm).
What is Inductive Learning? How can the performance of inductive learning algorithms be measured?
Discuss the classification approach in Machine Learning. Differentiate between classification and regression.
What is a Decision Tree? Explain its role in inductive learning with a suitable example. Define the types of nodes in a decision tree.
What do you mean by Support Vector Machine (SVM)? Discuss its features and applications in AI.
Explain the Nearest Neighbor rule used for classification in pattern recognition.
What is Clustering? Illustrate the K-Means clustering technique with a suitable example.
What do you mean by feature extraction in Machine Learning?
Define Principal Component Analysis (PCA). Differentiate between PCA and Linear Discriminant Analysis (LDA) for dimension reduction.
Define Reinforcement Learning. How does passive reinforcement learning differ from active reinforcement learning? Write its applications.
What is a Neural Network? Discuss Recurrent Neural Networks in detail.
What is the biggest advantage of Deep Learning? How does it mimic the human brain, and what are its limitations?
Write a short note on the Perceptron model. How is it related to networking in AI?
Explain data wrangling in Machine Learning.
State the basic steps of the mirror descent algorithm.

Unit 5

Applications of AI & Multi-Agent Systems

Describe the role of Artificial Intelligence in Natural Language Processing (NLP).
What are the various phases and stages of NLP in Artificial Intelligence?
Discuss the differences between Natural Language Understanding and Natural Language Generation. List the features that make Natural Language Understanding hard.
Explain Syntactic Analysis, Semantic Analysis, and Tokenization in NLP.
What is parsing in NLP? Differentiate between bottom-up parsing and other parsing techniques.
Explain the N-gram language model. Discuss the importance of pre-trained language models and transformers in modern NLP tasks.
Define Speech Act Theory. How will you define a speech act if the performative is "request" and the content is "the door is closed"?
What is Pattern Recognition? Explain the design principles of a pattern recognition system with a suitable example.
Give the block diagram of a pattern recognition system and explain parameter estimation methods.
Describe the various feature extraction and selection methods in pattern recognition.
Define Computer Vision. What is the difference between Human Vision and Computer Vision?
What features of images make computer vision a difficult process? Discuss the advantages and challenges of face recognition systems.
What are Image-Processing Operations? Define how vision can be used for navigation and manipulation of an object.
What is Speech Recognition? Illustrate how Hidden Markov Models (HMM) are used in speech recognition systems.
What is Machine Translation? Discuss the challenges of multilingual support and how it is helpful for differently-abled persons.
Discuss the concept of Information Retrieval versus Information Extraction. What are the challenges associated with large and unstructured datasets?
What is Robotics? Differentiate between a Robotic System and other AI programs.
Describe the various components, architecture, and configuration bases of a Robot. How does computer vision contribute to robotics?
Explain the communication paradigms used by intelligent agents in a multi-agent system.
Define the two types of Agent Communication Language (ACL).
Explain the Contract Net Protocol used for communication between multi-agent systems, including each stage of the protocol.
Explain the role of Trust, Reputation, Negotiation, Argumentation, and Bargaining in multi-agent systems.
How do cross-domain trust models contribute to more versatile and effective multi-agent interactions?
What is Explainable AI (XAI)? Discuss case studies of XAI in NLP and computer vision, and explain the role of interpretability in AI.
Explain the role of AI in enhancing decision-making processes in business applications.

General & Miscellaneous Questions

Explain the different types of Chomsky's hierarchy of grammars.
Differentiate between deterministic and non-deterministic environments.
Write short notes on Minsky frames.
Differentiate between an Expert System and a Problem-Solving System.
Write down the major features of the expert system "MYCIN". Explain the working of the "DENDRAL" expert system.
How does expertise transfer take place in an expert system? Explain the method of using an Expert System in Management Information Systems.
Discuss the limitations of Expert Systems and fault diagnosis.
Differentiate between Strong AI and Weak AI.
How is symbolic description different from structured description?
What is Conceptual Dependency? Write a short note on representing instance and ISA relationships.
Write an LISP program to compute prime numbers between 1 to 20.
Write an LISP program to convert Centigrade temperature to Fahrenheit. Why is LISP popular among AI practitioners?
What do you understand by Utility Theory? What advantages does it hold for AI?
What is the Universal Approximation Theorem?
What is Data Visualization? Explain in detail.
What are Chatbots? Explain their advantages.
Define the Skeletonization method and Voronoi graph.
Prove whether the following statements/arguments are valid or inconsistent:
- "Aman loves Priya and John is not happy but her parents are happy."
- "If prices fall then sell increases. If sell increases then John makes the whole money. But John doesn't make the whole money. Therefore, prices do not fall."
State the Karush-Kuhn-Tucker (KKT) conditions.
Define proximal gradient methods, monotone operators, and Langevin dynamics.
Explain the connection between ODE interpretations and optimization algorithms.
How does Polyak–Juditsky averaging smooth the optimization trajectory?
What impact will AI have on privacy and data security, and what measures should be in place to protect individuals in the future?
What ethical considerations should be taken into account in the development and deployment of AI systems?
Explain the role of AI in tackling global challenges like climate change and resource management.

Artificial Intelligence

Artificial Intelligence

-------------------------------------

Unit I

Introduction to Artificial Intelligence & Intelligent Agents :

Definition and scope of AI, History and applications of AI,

Characteristics of Intelligent Agents,

Types of agents and environments,

Agent architecture, Problem Solving Approach to Typical AI problems,

Problem-solving agents.

Example problems and approaches

-------------------------------------

Unit II

Problem Solving & Search Strategies:

Uninformed Search Strategies:

BFS,

DFS,

Iterative Deepening,

Informed Search Strategies:

Greedy Best-First Search,

A* Search, Heuristics and Optimization,

Hill Climbing, Simulated Annealing,

Constraint Satisfaction Problems,

Game Playing: Min-max,

Alpha-Beta Pruning,

Stochastic &

Partially Observable Games

-------------------------------------

Unit III

Knowledge Representation & Reasoning:

Propositional and First Order Logic,

Syntax,

Semantics,

and Inference,

Knowledge-based agents:

Wumpus world,

Logic Programming using Prolog,

Forward and Backward Chaining,

Resolution,

Ontological Engineering and Reasoning

-------------------------------------

Unit IV

Uncertainty & Learning Techniques:

Introduction to uncertainty and probabilistic reasoning,

Bayes' Rule,

Bayesian Networks,

Fuzzy logic and handling imprecision,

Neural Networks (basics only):

Perceptron,

Backpropagation (intro level),

Fundamentals of Machine Learning in AI context,

Introduction to supervised and

unsupervised learning

-------------------------------------

Unit V

Applications of AI & Multi-Agent Systems:

Natural Language Processing,

Machine Translation,

Information Retrieval and Extraction,

Robotics: Perception,

Planning, and Motion,

Speech Recognition,

Software Agents: Architecture,

Communication,

Trust,

Multi-agent Negotiation and Reputation.

Explainable AI (XAI) – Importance of interpretability,

techniques for explaining black-box models,

trust in AI,

case studies in NLP and vision.

-------------------------------------

Monday, February 24, 2025

Enqueue and dequeue in queue

#include <stdio.h>

#include <stdlib.h>

#define MAX 100

int queue[MAX];

int front = -1, rear = -1;

void enqueue(int value) {

if (rear == MAX - 1) {

printf("Queue Overflow\n");

return;

}

if (front == -1) {

front = 0;

}

queue[++rear] = value;

}

int dequeue() {

if (front == -1 || front > rear) {

printf("Queue Underflow\n");

return -1;

}

return queue[front++];

}

int main() {

enqueue(10);

enqueue(20);

enqueue(30);

printf("%d dequeued from queue\n", dequeue());

return 0;

}

Insert at beginning in Linked list

#include <stdio.h>

#include <stdlib.h>

struct Node {

int data;

struct Node* next;

};

void insertAtBeginning(struct Node** head, int value) {

struct Node* newNode = (struct Node*)malloc(sizeof(struct Node));

newNode->data = value;

newNode->next = *head;

*head = newNode;

}

void printList(struct Node* head) {

while (head != NULL) {

printf("%d -> ", head->data);

head = head->next;

}

printf("NULL\n");

}

int main() {

struct Node* head = NULL;

insertAtBeginning(&head, 10);

insertAtBeginning(&head, 20);

insertAtBeginning(&head, 30);

printList(head);

return 0;

}

Adding new element in linked list at the end.

#include <stdio.h>

#include <stdlib.h>

struct Node {

int data;

struct Node* next;

};

void insertAtEnd(struct Node** head, int value) {

struct Node* newNode = (struct Node*)malloc(sizeof(struct Node));

newNode->data = value;

newNode->next = NULL;

if (*head == NULL) {

*head = newNode;

return;

}

struct Node* temp = *head;

while (temp->next != NULL) {

temp = temp->next;

}

temp->next = newNode;

}

void printList(struct Node* head) {

while (head != NULL) {

printf("%d -> ", head->data);

head = head->next;

}

printf("NULL\n");

}

int main() {

struct Node* head = NULL;

insertAtEnd(&head, 10);

insertAtEnd(&head, 20);

insertAtEnd(&head, 30);

printList(head);

return 0;

}

Push and pop in stack

#include <stdio.h>

#include <stdlib.h>

#define MAX 100

int stack[MAX];

int top = -1;

void push(int value) {

if (top >= MAX - 1) {

printf("Stack Overflow\n");

return;

}

stack[++top] = value;

}

int pop() {

if (top < 0) {

printf("Stack Underflow\n");

return -1;

}

return stack[top--];

}

int main() {

push(10);

push(20);

push(30);

printf("%d popped from stack\n", pop());

return 0;

}

CREATIVITYMEETS CODE.

Latest from XmonoCodes

Friday, August 14, 2026

Artificial Intelligence (BCS701)

Unit-wise Question Bank & Previous Year Questions (AKTU)

Unit 1

Introduction to Artificial Intelligence & Intelligent Agents

Unit 2

Problem Solving & Search Strategies

Unit 3

Knowledge Representation & Reasoning

Unit 4

Uncertainty & Learning Techniques

Unit 5

Applications of AI & Multi-Agent Systems

General & Miscellaneous Questions

Monday, February 24, 2025

CREATIVITY
MEETS CODE.