About this previous year paper
Course: B.Tech (Semester V)
Subject Code: BCAI501
Subject: Artificial Intelligence
Maximum Marks: 70
Duration: 3 Hours
Paper ID: 310468
Section A (2 × 7 = 14 Marks)
Attempt all questions in brief:
Define Artificial Intelligence.
Define problem-solving in AI.
Define problem-solving in the context of AI.
What is a Constraint Satisfaction Problem (CSP)?
What is First Order Predicate Logic (FOPL)?
Define an intelligent agent.
Explain the concept of machine translation.
Section B (Attempt any three × 7 = 21 Marks)
Compare and contrast different types of Intelligent Agents – Simple Reflex, Model-Based, Goal-Based, and Utility-Based.
Explain the difference between uninformed and informed search strategies with examples.
Discuss different knowledge representation techniques used in AI.
Describe the negotiation process in multi-agent systems with examples.
Discuss the process and importance of information retrieval in AI.
Section C (Attempt any one part from each question – 7 Marks each)
Q3
(a) Explain the evolution of Artificial Intelligence from symbolic AI to modern machine learning.
OR
(b) Explain the role of AI in enhancing decision-making in business applications.
Q4
(a) Discuss the structure and components of a Constraint Satisfaction Problem (CSP).
OR
(b) Describe how to solve a Sudoku puzzle using CSP methods.
Q5
(a) Compare semantic networks and frames as knowledge representation methods.
OR
(b) Explain how reasoning systems are designed for decision-making in AI.
Q6
(a) Explain how argumentation is used for resolving conflicts in multi-agent systems.
OR
(b) Explain how autonomous agents manage decision-making and execution.
Q7
(a) Explain the steps involved in extracting structured information from unstructured data.
OR
(b) Explain how tokenization impacts natural language processing tasks.
Key Topics for Revision
Artificial Intelligence fundamentals and history
Problem-solving approaches and search strategies
Constraint Satisfaction Problems (CSPs)
Intelligent Agents – types and behavior
Knowledge representation techniques (Semantic networks, Frames, Logic)
Multi-agent systems, negotiation, and argumentation
Machine learning and reasoning systems
NLP basics – tokenization and machine translation