DCA6301 ARTIFICIAL INTELLIGENCE JAN FEB 2026

Description

SESSION	FEB-MARCH 2026
PROGRAM	MASTER OF COMPUTER APPLICATIONS (MCA)
SEMESTER	II
course CODE & NAME	DCA6301 Artificial Intelligence

 

 

Assignment Set – 1

 

Q.1. (a) Define Artificial Intelligence. Explain its major goals and real-world applications. (b) What are intelligent agents? Classify different types of agents and explain their working with practical examples.

Ans 1.

(a) Artificial Intelligence: Definition, Goals, and Applications

Artificial Intelligence is the branch of computer science concerned with making systems that are able to perform the tasks that usually need human brains. These tasks include reasoning through experience, learning, comprehending language, seeing patterns and making choices. The term was coined by John McCarthy in 1956 at the Dartmouth Conference, and since it was coined, AI

Q.2. (a) Differentiate between propositional logic and first-order logic. Provide suitable problem-based examples. (b) Compare supervised, unsupervised, and reinforcement learning in terms of working principles, use-cases, and limitations.

Ans 2.

(a) Propositional Logic vs First-Order Logic

Propositional logic, also known as zeroth-order logical reasoning, deals with the propositions and statements that can be either true or not. It uses logical connectives like AND, OR, NOT or IF in order to transform simple sentences into complicated ones. As an example, the sentence “It’s

 

Q.3. (a) What is Natural Language Processing? Explain the roles of syntax, semantics, and pragmatics in NLP. (b) Describe fuzzy sets and fuzzy inference systems. Demonstrate their application using a real-life scenario.

Ans 3.

 (a) Natural Language Processing: Syntax, Semantics, and Pragmatics

Natural Language Processing is a branch of AI that enables computers to understand, interpret, and create human languages. NLP can bridge the gaps between human language and machine understanding. It is a combination of linguistics, computer science, and machine understanding

 

Assignment Set – 2

 

Q.4. (a) Explain feature extraction in computer vision. How does it impact model performance? Illustrate with an example. (b) What are heuristic search techniques? Compare any two algorithms based on efficiency and application.

Ans 4.

(a) Feature Extraction in Computer Vision

A process called feature extraction is of extracting and separating the most pertinent information of raw image data, so that a machine learning model can learn patterns effectively. Raw images contain many millions of pixels. Processing them directly is costly computationally and usually

 

Q.5. (a) Describe the role of sensors and actuators in robotics. (b) Explain Q-learning. How is it used in reinforcement learning?

Ans 5.

(a) Sensors and Actuators in Robotics

The robot communicates with its surroundings through two essential components: sensors, which collect information about the surroundings, and actuators, which let the robot take action in that environment. Together, they make up the perception-action loop that can enable intelligent

 

Q.6. (a) Discuss major AI applications in business and industry with examples. (b) Discuss ethical challenges in AI systems with respect to bias, fairness, transparency, and privacy.

Ans 6.

(a) AI Applications in Business and Industry

Artificial Intelligence has transformed how companies operate in virtually all industries. For e-commerce and retail, recommendations engines study browsing histories purchases, patterns of purchase, as well as demographic data to suggest products. Amazon’s recommendation engine is