RTU Kota B.Tech 6th Semester Principles of Artificial Intelligence Question Paper 2025 (CSE/IT/AI)

RTU Principles of Artificial Intelligence 2025 Paper Review

Preparing for the Rajasthan Technical University B.Tech Principles of Artificial Intelligence exam requires a firm grasp of state-space search algorithms, formal logic representations, and adversarial game playing. For developers structuring backends using Node.js or optimizing database queries, understanding how intelligent agents navigate constraints and make optimal decisions forms the core of computational logic. You cannot build complex, autonomous software without understanding how heuristic functions guide a machine toward a goal.

The 2025 paper tests your capability to trace A* search iterations, construct first-order logic proofs using resolution, and execute alpha-beta pruning on game trees. Publishing this specific 6th-semester paper review directly to exam-support.in provides engineering students exactly what they need to understand how examiners construct algorithmic problems and distribute marks across the core AI modules. This targeted preparation strategy helps approach the exam confidently, Jaiprakash.

Understanding the Exam Pattern

The RTU theory examination is a three-hour paper worth 70 marks. The paper features three distinct sections designed to evaluate both foundational AI definitions and complex algorithmic execution.

• Part A: This section contains ten compulsory questions worth two marks each. You must define terms like rational agent, state the difference between forward and backward chaining, define a heuristic function, or explain the Turing Test under 30 words.
• Part B: You will find seven questions here. You must answer five of them. Each question is worth four marks. Your answers require executing short logic proofs, explaining the components of an expert system, or tracing a basic Breadth-First Search queue progression.
• Part C: This section offers five major questions. You need to answer three. Each question carries ten marks. These require you to execute the complete Minimax algorithm with Alpha-Beta pruning on a provided game tree, solve a cryptarithmetic problem by explicitly defining the constraint satisfaction variables, or translate complex English sentences into First-Order Predicate Logic and prove a theorem using resolution.

Core Topics Evaluated in the Paper

The 2025 question paper covers several critical modules that establish the mathematical rules for machine reasoning. Focus your study time on these specific areas to maximize your score.

Problem Solving and State Space Search

This module evaluates your understanding of how AI navigates environments. You must master both uninformed (BFS, DFS, Uniform Cost) and informed search strategies. The heaviest focus lies on the A* Algorithm. You must know how to calculate the total estimated cost $f(n) = g(n) + h(n)$. Practice drawing the search tree and maintaining the Open and Closed lists step-by-step for a given graph matrix. Study the conditions for heuristic admissibility and consistency.

Knowledge Representation and Logic

Machines require formal structures to store and process facts. You must master Propositional Logic and First-Order Predicate Logic (FOPL). Practice translating standard English statements into logical formulas using universal ($\forall$) and existential ($\exists$) quantifiers. The 2025 paper features major questions requiring you to convert logical statements into Conjunctive Normal Form (CNF) and execute a proof by contradiction using the Resolution algorithm.

Adversarial Search and Game Playing

When two intelligent agents compete, they must anticipate opponent moves. You must understand the Minimax search algorithm. Practice tracing the exact utility values passed up from the terminal nodes to the root. Furthermore, you must master Alpha-Beta Pruning. Learn how to track the $\alpha$ (best choice for MAX) and $\beta$ (best choice for MIN) values down the tree and explicitly cross out the branches that the algorithm prunes to save computation time.

Planning and Constraint Satisfaction

This module focuses on achieving goals through sequences of actions. Study the STRIPS representation language, focusing on preconditions, add lists, and delete lists. You must also understand Constraint Satisfaction Problems (CSPs). Practice defining the variables, domains, and constraints for classic problems like Map Coloring, the 8-Queens problem, and Cryptarithmetic puzzles.

Expert Systems and Uncertainty

Expert systems mimic human decision-making in narrow domains. You must draw the architecture of an expert system, labeling the knowledge base, inference engine, and user interface. For reasoning under uncertainty, study basic probabilistic logic. The paper frequently asks for straightforward calculations using Bayes' Theorem to determine conditional probabilities.

Answer Writing Strategy for High Marks

RTU evaluators look for clean search trees, explicitly stated heuristic values, and step-by-step logical proofs. Use a blue pen for text explanations and calculation lines. Use a black pen and ruler for drawing state-space graphs, game trees, and expert system architectures.

In Part A, answer directly. If a question asks for the definition of an admissible heuristic, state clearly that it is a heuristic function that never overestimates the true cost to reach the goal state from the current node.

In Part B, use clear computation structures. When translating statements to First-Order Logic, write down the predicate definitions (e.g., Let $M(x)$ represent "x is mortal") before writing the final mathematical formulation to make your logic visually scannable for the checker.

In Part C, precision in algorithmic tracing is critical. When solving a ten-mark Alpha-Beta pruning problem, draw the complete game tree. Write the $[\alpha, \beta]$ values explicitly next to every single node. Update these values clearly as you trace the depth-first traversal, and draw distinct slash marks through the branches that are pruned, explaining the exact mathematical inequality (e.g., $\alpha \ge \beta$) that triggered the cut-off.

Time Management During the Exam

Allocate exactly 20 minutes to Part A. Spend 40 minutes addressing the five short-answer questions in Part B. Reserve the remaining 120 minutes for the three long-answer questions in Part C. Drawing multi-level game trees, executing step-by-step resolution refutations, and calculating heuristic lists requires steady focus and significant writing time to prevent tracking mistakes. This distribution guarantees you 40 minutes per major question, giving you time to double-check your logical conversions. Use the final 10 minutes to verify your question numbering, ensure all tree edges indicate the correct direction, and check that your A* priority queue matches the calculated costs.