18 solved GATE DA questions on Artificial Intelligence, drawn from 3 exam years and grouped by year. Every question shows the official answer and a step-by-step solution.
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Specify the state representation, actions, goal test, and cost model before choosing a search strategy. Logical inference requires valid rules applied to a stated knowledge base. These selected foundations connect search algorithms, propositional and first-order logic, constraint satisfaction, and planning. They are not a complete syllabus.
Our study notes and original examples support the PYQs below; they are not official exam questions or a replacement for the current syllabus.
BFS explores nodes in FIFO order and finds the shallowest goal; it is complete and optimal for unit-cost edges but uses O(b^d) memory. DFS explores depth-first using LIFO and uses O(bd) memory but is neither complete in infinite spaces nor optimal. Iterative deepening combines DFS memory efficiency with BFS completeness.
Check yourself: Is the search space finite, and does the cost model match the strategy's guarantee?
A* expands the node with the smallest f(n) = g(n) + h(n), where g is the path cost so far and h is a heuristic estimate to the goal. A* is optimal when h is admissible (never overestimates). Consistency (h(n) ≤ cost(n, n′) + h(n′)) ensures nodes are not re-expanded. A non-admissible heuristic can find suboptimal solutions.
Check yourself: Does your heuristic value ever exceed the true remaining cost for any node?
An implication P → Q is false only when P is true and Q is false. Modus Ponens derives Q from P and P → Q. Resolution works on clausal form: resolve two clauses sharing complementary literals. First-order logic adds quantifiers: ∀x P(x) requires P to hold for every domain element; ∃x P(x) requires at least one.
Check yourself: Have you converted to clausal form before applying resolution?
A CSP has variables, domains, and constraints. Backtracking search assigns variables one at a time and backtracks on constraint violation. Arc consistency prunes domain values that have no supporting assignment in a neighboring variable. Forward checking removes inconsistent values from unassigned neighbors after each assignment.
Check yourself: After pruning, does every remaining value in each domain participate in at least one consistent assignment with each neighbor?
Planning represents states as sets of propositions and actions as precondition-effect pairs. An action is applicable when its preconditions hold in the current state; applying it adds its positive effects and removes its negative effects. Forward search applies actions from the initial state toward the goal; backward search regresses the goal through relevant actions.
Check yourself: Are all preconditions of the action satisfied in the current state before applying it?
Original teaching example · not a PYQ
Original example: apply A* to find the shortest path from S to G. Edges: S→A (cost 1), S→B (cost 4), A→B (cost 2), A→G (cost 6), B→G (cost 3). Heuristic: h(S)=5, h(A)=4, h(B)=2, h(G)=0. Verify admissibility.
Optimal path: S → A → B → G with cost 1 + 2 + 3 = 6.
Separate original check: a CSP has variables X, Y, Z each with domain {1, 2, 3} and constraints X ≠ Y, Y ≠ Z, and X < Z. How many solutions exist? List them.
Three solutions: (1, 3, 2), (1, 2, 3), (2, 1, 3).
X < Z restricts pairs to (X,Z) ∈ {(1,2), (1,3), (2,3)}. For (1,2): Y ≠ 1 and Y ≠ 2, so Y = 3 → (1,3,2). For (1,3): Y ≠ 1 and Y ≠ 3, so Y = 2 → (1,2,3). For (2,3): Y ≠ 2 and Y ≠ 3, so Y = 1 → (2,1,3). All three satisfy every constraint.
Supplemental reading, not an official GATE reading list or an endorsement of these notes.
Free supplemental lectures and assignments covering search, logic, constraint satisfaction, and planning from MIT's introductory AI course.
This page shows 18 recent questions from the released archive, newest first. For older questions and complete papers, browse all GATE DA papers. Questions can carry more than one subject tag; counts are not marks weightage.
Task column lists a few tasks related to machine learning. The Algorithm column lists a few algorithms. Each entry “t” from the…MSQ · +1 marks · EasyContinue learning with Success Tracker
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