RTU Kota B.Tech 6th Semester Principles of Artificial Intelligence Question Paper 2023 (CSE/AI/IT)

RTU Principles of Artificial Intelligence 2023 Paper Review

The 2023 Principles of Artificial Intelligence (PAI) examination at Rajasthan Technical University (RTU) focused on the core transition from foundational agent theory to complex logic-based reasoning. For students in CSE, AI, and IT, the 2023 curriculum emphasized the balance between theoretical problem-solving (search algorithms) and formal logic representations.

This review breaks down the structure of the 2023 paper to help you understand the examiners' priorities and refine your preparation strategy.

Understanding the Exam Pattern

The RTU theory examination is a three-hour paper worth 70 marks, organized into five modules:

• Part A (Modules I & II): Compulsory questions focusing on foundational AI concepts. Expect definitions of AI versus conventional computing, knowledge engineering steps, and reasoning methods.
• Part B (Modules III, IV, & V): Choice-based questions. You are typically required to answer one out of two questions for each module, allowing you to focus on specific strengths in state-space search, game theory, logic, or planning.
• Question Format: Each question is worth 7 marks. They often combine theoretical explanations with small practical traces, such as state search problems or game tree development.

Core Topics Evaluated in the 2023 Paper

Focus your study time on these specific modules to maximize your score:

1. AI Foundations and Knowledge Representation

• Key Focus: Distinguishing AI from conventional computing, and mastering the Knowledge Engineering process.
• Logic: Be prepared to illustrate instance and isa relationships in predicate logic and explain how to add exceptions to knowledge bases.

2. Reasoning and Search Strategies

• Search: A significant portion of the marks. You must be able to trace bread-first (BFS), uniform cost, depth-first (DFS), and A* search algorithms for given state-space graphs.
• Games: Minimax algorithm and Alpha-Beta pruning are staples. Practice developing game trees with specific depth and branching factors.

3. Handling Uncertainty

• Logic: Classifying different types of non-monotonic logic and discussing the ethical implications of biased defaults.
• Certainty: Understanding how certainty factors are used in rule-based expert systems to manage imprecise data.

4. Learning and Planning

• Reinforcement Learning: Basic concepts of passive versus active reinforcement learning.
• Planning: Comparing linear versus non-linear planning strategies and using the goal set method.
• Learning: Differences between ROTE learning and learning by taking advice.

Answer Writing Strategy for High Marks

RTU evaluators prioritize clarity, technical accuracy, and structured communication.

• Diagrams: Use a ruler and black pen for tree diagrams, game trees, and state-space graphs. Clear labels are essential for full marks in search problems.
• Formatting: Use headings and bullet points for your explanations. For questions requiring a comparison, always use a table.
• Precision: If the question involves an algorithm, state the objective, provide the pseudocode or steps, and show a small example or trace.
• Logical Flow: In logic proofs or reasoning questions, clearly state your assumptions and follow the formal steps of the resolution process.

Time Management During the Exam

• Sectional Pacing: With 70 marks and 180 minutes, you have roughly 2.5 minutes per mark. Allocate your time to ensure all compulsory questions are answered thoroughly before moving to the choice-based modules.
• Part C Strategy: Dedicate roughly 35-40 minutes to each of the three choice-based modules. This ensures you have time to draw your diagrams and perform your algorithmic traces without rushing.
• Review: Reserve the final 10 minutes to verify your question numbering and check that all parts of the multi-part questions have been addressed.