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    Adversarial Search, Minimax and Alpha-Beta Pruning PYQs for GATE DA

    Solve 3+ Adversarial Search, Minimax and Alpha-Beta Pruning previous year questions for GATE DA with answers and detailed solutions. Free sample questions bel

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    Question 1
    2026 PYQ
    Consider the game tree for a two-player turn-taking minimax game as shown in the figure. The value of a terminal node represents the utility of the game state if the game ends there. The numbers written next to the edges denote the strategies.

    There are two players MAX and MIN. At any particular state of the game, MAX prefers to move to a state of maximum value. On the other hand, MIN prefers to move to a state of minimum value.

    Suppose MAX starts the game at the root and has three strategies: 1, 2 and 3. Next, MIN plays and also has three strategies: 1, 2 and 3. The game ends there. Both players always take optimal strategies throughout the game.

    At the root, the best strategy for MAX is ___________ . (Answer in integer)
    MAX MIN 1 2 3 1 2 3 8 6 −1 1 2 3 1 5 7 1 2 3 −4 −3 −12
    Question 2
    2025 PYQ
    Consider game trees Tree-1 and Tree-2 as shown. The first level is a MAX agent and the second level is a MIN agent. The value in the square node is the output of the utility function.

    MAX MIN A 2 B x 1 Tree-1 C D E 5 y 2 Tree-2
    For what ranges of and , the right child of node and the right child of node will be pruned by alpha-beta pruning algorithm?
    Question 3
    2024 PYQ
    Consider the following statement:
    In adversarial search, – pruning can be applied to game trees of any depth where
    is the (m) value choice we have formed so far at any choice point along the
    path for the MAX player and is the (n) value choice we have formed so far
    at any choice point along the path for the MIN player.
    Which ONE of the following choices of (m) and (n) makes the above statement
    valid?
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    Adversarial Search, Minimax and Alpha-Beta Pruning PYQs for GATE DA

    Solve 3+ Adversarial Search, Minimax and Alpha-Beta Pruning previous year questions for GATE DA with answers and detailed solutions. Free sample questions below.

    Chapter Roadmap: Adversarial Search

    1
    Minimax Game Tree Evaluation and Strategy Selection
    Foundation of adversarial search. Learn how MAX and MIN agents propagate utility values from terminal states to the root to select optimal strategies in deterministic, perfect-information games.
    Importance: High | Core Dependency for all game-playing AI
    2
    Alpha-Beta Pruning Bounds and Interpretation
    Optimization of minimax. Learn to maintain alpha and beta bounds to identify and prune branches that cannot possibly influence the final decision, without changing the final result.
    Importance: Moderate to High | Frequent Exam Application

    Topic Hero: The Minimax Intuition

    The Adversarial Setting

    Minimax applies strictly to games with three properties:

    • Two-player: Exactly two agents taking turns.
    • Zero-sum: The utility of the game is strictly opposed. If MAX gains , MIN effectively gains .
    • Perfect information: No hidden states or chance elements (no dice, no hidden cards).

    The Core Assumption

    The algorithm is built on a single, critical premise: The opponent plays optimally. When MAX evaluates a move, it does not hope for MIN to make a mistake. It assumes MIN will always choose the branch that yields the lowest possible utility for MAX. Therefore, MAX's strategy is to maximize the minimum possible payoff (hence, "Minimax").

    Adversarial Search, Minimax and Alpha-Beta Pruning: Solved Questions with Step-by-Step Explanations (3 Problems)

    Question 1 · Artificial Intelligence · 2026 NAT
    Consider the game tree for a two-player turn-taking minimax game as shown in the figure. The value of a terminal node represents the utility of the game state if the game ends there. The numbers written next to the edges denote the strategies.

    There are two players MAX and MIN. At any particular state of the game, MAX prefers to move to a state of maximum value. On the other hand, MIN prefers to move to a state of minimum value.

    Suppose MAX starts the game at the root and has three strategies: 1, 2 and 3. Next, MIN plays and also has three strategies: 1, 2 and 3. The game ends there. Both players always take optimal strategies throughout the game.

    At the root, the best strategy for MAX is ___________ . (Answer in integer)
    MAX MIN 1 2 3 1 2 3 8 6 −1 1 2 3 1 5 7 1 2 3 −4 −3 −12
    Question 2 · Artificial Intelligence · 2025 MCQ
    Consider game trees Tree-1 and Tree-2 as shown. The first level is a MAX agent and the second level is a MIN agent. The value in the square node is the output of the utility function.

    MAX MIN A 2 B x 1 Tree-1 C D E 5 y 2 Tree-2
    For what ranges of and , the right child of node and the right child of node will be pruned by alpha-beta pruning algorithm?
    1. A.

      and

    2. B.

      and

    3. C.

      and

    4. D.

      and

    Question 3 · Artificial Intelligence · 2024 MCQ
    Consider the following statement:
    In adversarial search, – pruning can be applied to game trees of any depth where
    is the (m) value choice we have formed so far at any choice point along the
    path for the MAX player and is the (n) value choice we have formed so far
    at any choice point along the path for the MIN player.
    Which ONE of the following choices of (m) and (n) makes the above statement
    valid?
    1. A.

      (m) = highest, (n) = highest

    2. B.

      (m) = lowest, (n) = highest

    3. C.

      (m) = highest, (n) = lowest

    4. D.

      (m) = lowest, (n) = lowest

    More previous year questions (pyqs) in this unit