Breadth-First Search as an Uninformed Search Strategy

Breadth-First Search, or BFS, is an example of (ii) Uninformed Search, also called blind search.2 It explores the state space level by level, always expanding the shallowest frontier node before moving deeper.2 Because BFS relies only on the problem definition—initial state, actions, transition model, and goal test—and does not use any heuristic function such as $h(n)$, it is not an informed, heuristic, or local search method.2

In artificial intelligence, this classification matters because search methods are often distinguished by what information guides node expansion. Uninformed methods like BFS and DFS use no estimate of how close a node is to the goal, whereas informed methods such as greedy best-first search and A* use heuristic guidance.2 Local search, by contrast, typically keeps only a single current state and moves to neighboring states without systematically storing full search paths, which is very different from BFS’s frontier-based exploration.

A concise decision statement is therefore:

$$\boxed{\text{Breadth-First Search is an example of Uninformed Search}}$$

3

Footnotes

1. 3 SOLVING PROBLEMS BY SEARCHING - Pearson Higher Education - Russell and Norvig text describing BFS as expansion of the shallowest node using a FIFO queue. ↩ ↩² ↩³

2. Lecture 2: Uninformed search methods Search in AI - Explains uninformed search as search without knowledge of closeness to the goal and places BFS in that category. ↩ ↩² ↩³ ↩⁴

3. 1.3 Uninformed Search | Introduction to Artificial Intelligence - Defines BFS as selecting the shallowest frontier node and using a FIFO queue. ↩

4. 3.6 Informed (Heuristic) Search ‣ Artificial Intelligence: Foundations of Computational Agents - Distinguishes heuristic/informed search from uninformed methods through the use of heuristic evaluation. ↩ ↩² ↩³

5. Informed search algorithms - Describes local search as operating on a single current node and moving to neighbors without retaining full paths. ↩

To understand why BFS is uninformed, it helps to compare the four options in the question.

This distinction can be formalized in terms of node selection. In BFS, the next node is chosen by minimum depth, not by a heuristic score.2 If all step costs are equal, BFS returns a shallowest solution, which also makes it optimal under unit-cost assumptions.2 However, if step costs vary, BFS may fail to find the least-cost path; in that case, uniform-cost search is preferred.2

Footnotes

1. Lecture 2: Uninformed search methods Search in AI - Explains uninformed search as search without knowledge of closeness to the goal and places BFS in that category. ↩ ↩²

2. 3.6 Informed (Heuristic) Search ‣ Artificial Intelligence: Foundations of Computational Agents - Distinguishes heuristic/informed search from uninformed methods through the use of heuristic evaluation. ↩ ↩²

3. 3 SOLVING PROBLEMS BY SEARCHING - Pearson Higher Education - Russell and Norvig text describing BFS as expansion of the shallowest node using a FIFO queue. ↩ ↩² ↩³

4. 1.3 Uninformed Search | Introduction to Artificial Intelligence - Defines BFS as selecting the shallowest frontier node and using a FIFO queue. ↩ ↩² ↩³

5. Informed search algorithms - Describes local search as operating on a single current node and moving to neighbors without retaining full paths. ↩

6. Artificial Intelligence Search Agents Uninformed search - Summarizes BFS properties including completeness, optimality under unit cost, and $O(b^d)$ time and space. ↩ ↩²

Core properties of BFS

BFS is widely taught in AI because it has clear theoretical properties. It is complete when the branching factor is finite, meaning it will eventually find a solution if one exists at some finite depth.2 It is also optimal for equal step costs, because the first goal found is at the shallowest depth.2 Its main weakness is memory use: both time and space can grow exponentially with depth, often written as $O(b^d)$ in tree-style analysis, where $b$ is the branching factor and $d$ is the shallowest goal depth.2

In graph-theoretic analysis on an explicit graph with vertices and edges, BFS runs in $O(|V|+|E|)$ time and uses $O(|V|)$ extra space. In AI search-tree analysis, however, the more common emphasis is on the exponential frontier growth in terms of $b$ and $d$.2

$$\text{Tree-search BFS time} \approx O(b^d), \qquad \text{space} \approx O(b^d)$$

This is why BFS is best suited when:

• the goal is expected to be shallow,
• step costs are uniform,2
• and sufficient memory is available.2

Footnotes

1. 3 SOLVING PROBLEMS BY SEARCHING - Pearson Higher Education - Russell and Norvig text describing BFS as expansion of the shallowest node using a FIFO queue. ↩ ↩² ↩³ ↩⁴

2. Artificial Intelligence Search Agents Uninformed search - Summarizes BFS properties including completeness, optimality under unit cost, and $O(b^d)$ time and space. ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

3. Uninformed search strategies (Section 3.4) - Notes BFS finds paths with the fewest steps but not always the cheapest under varying costs. ↩

4. Breadth-first search - Wikipedia - Provides standard graph-theoretic complexity of $O(|V|+|E|)$ time and $O(|V|)$ space for explicit graph traversal. ↩

5. 1.3 Uninformed Search | Introduction to Artificial Intelligence - Defines BFS as selecting the shallowest frontier node and using a FIFO queue. ↩

A compact exam-ready answer

If this appears as a multiple-choice question, the ideal answer is:

Breadth First Search is an example of (ii) Uninformed Search.2

A stronger explanatory answer would be:

BFS is an uninformed search algorithm because it explores the search space level by level using a FIFO queue and does not use heuristic information to estimate which node is closer to the goal.3

This explanation is often sufficient in academic exams, interviews, and introductory AI coursework.

Footnotes

1. 3 SOLVING PROBLEMS BY SEARCHING - Pearson Higher Education - Russell and Norvig text describing BFS as expansion of the shallowest node using a FIFO queue. ↩ ↩²

2. Lecture 2: Uninformed search methods Search in AI - Explains uninformed search as search without knowledge of closeness to the goal and places BFS in that category. ↩ ↩²

3. 1.3 Uninformed Search | Introduction to Artificial Intelligence - Defines BFS as selecting the shallowest frontier node and using a FIFO queue. ↩