Uninformed Search and Heuristic Search Practice Questions for GATE DA: 0+ Solved Questions with Step-by-Step Solutions

Solve 0+ Uninformed Search and Heuristic Search practice questions for GATE DA with answers and detailed solutions. Free sample questions below.

Chapter Roadmap: Search Strategies in AI

Chapter Journey: From Blind Search to Smart Search

TOPIC 1: Uninformed Search (Current)

BFS, DFS & Strategy Classification

Learn how computers explore state spaces blindly. Understand the trade-offs between Breadth-First and Depth-First search.

Weightage Hint: High Foundation

TOPIC 2: Heuristic Search (Next)

A* Search & Admissible Heuristics

Introduce smart guesses, called heuristics, to guide search. Master the A-star algorithm and the concept of admissibility.

What you will master in this topic:

The mechanics of BFS and DFS.
How to classify search strategies based on completeness, optimality, time, and space.
Solving complex tracing questions on state expansion with custom constraints.

The Search Problem: States, Actions, and Goals

Defining the Search Space

To solve any problem using search, we need four components:

Initial State: Where you start ( $s_{0}$ ).
Actions: What moves are available? For a state $s$ , $A c t i o n s (s)$ returns possible moves.
Transition Model: Where do you end up? $R es u l t (s, a)$ gives the new state after action $a$ .
Goal Test: Is this the solution? $I s G o a l (s)$ returns true or false.

The State Space Graph

Nodes: Represent states.
Edges: Represent actions with costs.
Path: A sequence of states from start to goal.

Key Insight: Search algorithms differ only in which node they choose to expand next.

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