Subject Code: KAI501
Subject Name: Artificial Intelligence
Course: B.Tech (Semester V)
Maximum Marks: 100
Duration: 3 Hours
Exam Year: 2023–24
Sections: A, B, and C
SECTION A – Short Answer Questions (2 × 10 = 20 Marks)
Attempt all questions briefly. Each carries 2 marks.
a. Explain how Artificial Intelligence impacts everyday life.
b. Explain the concept of default reasoning in AI.
c. Explain the concept of Skolemization.
d. Discuss decision-making in the context of game-playing algorithms.
e. Explain the Uniform-cost search algorithm.
f. Define backtracking in the context of search algorithms.
g. Define existential generalization in the context of first-order logic (FOPL).
h. Explain the importance of consistency in logical systems.
i. Discuss the consideration of temporal aspects in intelligent agent architecture.
j. Define the decision-making module in the architecture of intelligent agents.
SECTION B – Medium-Length Questions (10 × 3 = 30 Marks)
Attempt any three of the following (each 10 marks).
Discuss how a specific AI approach has been successfully applied in a real-world scenario, outlining challenges and outcomes.
How does the representation of objects contribute to the development of structured ontologies?
How can cross-domain trust models contribute to more effective multi-agent interactions?
Discuss the limitations of AI performance metrics and propose alternative evaluation methods.
What impact will AI have on privacy and data security, and what measures can protect individuals in the future?
SECTION C – Long / Analytical Questions (10 × 5 = 50 Marks)
Attempt one part from each question.
Q3. Default Reasoning & Search Algorithms
a. Explain the concept of default reasoning in AI.
OR
b. Provide examples of scenarios where alpha-beta pruning effectively speeds up decision-making in search algorithms.
Q4. AI Challenges & Ontologies
a. Identify challenges in advanced AI systems and suggest strategies to address them in problem-solving.
OR
b. Discuss challenges and considerations in modeling dynamic processes and events within ontological frameworks.
Q5. Game Theory & NLP
a. Discuss how Nash Equilibrium and Cooperative Game Theory apply to negotiation scenarios.
OR
b. Discuss how neural networks, especially transformers, have revolutionized NLP tasks.
Q6. Learning & Problem Solving
a. Provide examples of how supervised or reinforcement learning enhances AI decision-making capabilities.
OR
b. Discuss why a well-defined problem representation is crucial to the success of backtracking algorithms.
Q7. Logic & Machine Translation
a. Explore recent advances or extensions to FOPL (First-Order Predicate Logic) that address limitations and improve usability.
OR
b. Discuss emerging trends in Machine Translation, potential advancements, and future challenges.
Key Topics to Prepare
Core AI Concepts
Definition and real-life applications of AI Default reasoning, non-monotonic logic
Skolemization and existential generalization Consistency and completeness in logic
Search Algorithms
Uniform-cost search Backtracking, Alpha–Beta pruning
Heuristic and informed search
Knowledge Representation & Reasoning
Ontologies, semantic networks, and FOPL Temporal reasoning and agent architecture
Cross-domain trust in multi-agent systems
Game Theory & Decision Making
Nash equilibrium and cooperative models Decision modules in intelligent agents
AI in NLP and Learning
Transformers in NLP Supervised vs reinforcement learning
Advances in Machine Translation
Study Tips
Revise logic and reasoning concepts (FOPL, Skolemization, Default reasoning).
Draw algorithm flowcharts (Uniform-cost, backtracking, Alpha–Beta).
Practice examples for real-world AI applications (autonomous vehicles, recommendation systems).
Understand hybrid learning models — where supervised and reinforcement methods merge.
Focus on short-answer clarity — definitions and one-line formulas for Section A.
Review current AI advancements (Transformers, ChatGPT, BERT, etc.) for essay-type questions.
