Your question seems to be a bit unclear. However, I can provide information on Breadth-first search (BFS).

Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a 'search key') and explores the neighbor nodes at the present depth prior to moving on to nodes at the next depth level.

BFS is optimal when all the step costs are equal. This is because it explores all neighboring nodes at the current depth before moving on to nodes at the next depth level. Therefore, the first time a node is discovered during the traversal, that distance from the source would give us the shortest path. So, if there is a solution of depth d, BFS will find a solution of depth at most d. This is optimal when step cost is equal because all actions are identical in cost.

However, if step costs are not equal, BFS is not optimal as it may not find the shortest path to the goal. In such cases, algorithms like Uniform-cost search or A* can be used which consider the path cost for finding the optimal solution.

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Your question seems to be a bit unclear. However, I can provide information on Breadth-first search (BFS).

Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a 'search key') and explores the neighbor nodes at the present depth prior to moving on to nodes at the next depth level.

BFS is optimal when all the step costs are equal. This is because it explores all neighboring nodes at the current depth before moving on to nodes at the next depth level. Therefore, the first time a node is discovered during the traversal, that distance from the source would give us the shortest path. So, if there is a solution of depth d, BFS will find a solution of depth at most d. This is optimal when step cost is equal because all actions are identical in cost.

However, if step costs are not equal, BFS is not optimal as it may not find the shortest path to the goal. In such cases, algorithms like Uniform-cost search or A* can be used which consider the path cost for finding the optimal solution.

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Your question seems to be a bit unclear. However, I can provide information on Breadth-first search (BFS).

Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a 'search key') and explores the neighbor nodes at the present depth prior to moving on to nodes at the next depth level.

BFS is optimal when all the step costs are equal. This is because it explores all neighboring nodes at the current depth before moving on to nodes at the next depth level. Therefore, the first time a node is discovered during the traversal, that distance from the source would give us the shortest path. So, if there is a solution of depth d, BFS will find a solution of depth at most d. This is optimal when step cost is equal because all actions are identical in cost.

However, if step costs are not equal, BFS is not optimal as it may not find the shortest path to the goal. In such cases, algorithms like Uniform-cost search or A* can be used which consider the path cost for finding the optimal solution.

Breadth-first search is not optimal when all step costs are equal, because it always expands the shallowest unexpanded node.ans.IrrelevantFalseTrueexpanded Previous Marked for Review Next

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