Mention the difference between breadth first search and best first search in artificial intelligence?

please explain the difference between breadth and best first search

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  • The analogy that I like to consult when comparing such algorithms is robots digging for gold.
    Given a hill, Our goal is to simply find gold.
    Breadth-first search has no prior knowledge of the whereabouts of the gold so the robot simply digs 1 foot deep along the 10-foot strip if it doesn't find any gold, it digs 1 foot deeper.

    Best-first search, however, has a built-in metal detector, thus meaning it has prior knowledge. There is, of course, the cost in having a metal detector, and cost in turning it on and seeing which place would be the best to start digging.
    Best-first search is informed whereas Breadth-first search is uninformed, as in one has a metal detector and the other doesn't!

    Breadth-first search is complete, meaning it'll find a solution if one exists, and given enough resources will find the optimal solution.
    Best-first search is also complete provided the heuristic — estimator of the cost/ so the prior knowledge — is admissible — meaning it overestimates the cost of getting to the solution)
    Both of these are very popular searching algorithm for Artificial Intelligence.
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  • Breadth-first search has no prior knowledge of the whereabouts of the gold so the robot simply digs 1 foot deep along the 10-foot strip if it doesn't find any gold, it digs 1 foot deeper. Best-first search, however, has a built-in metal detector, thus meaning it has prior knowledge. There is, of course, the cost in having a metal detector, and cost in turning it on and seeing which place would be the best to start digging.
    Best-first search is whereas Breadth-first search is uninformed, as in one has a metal detector and the other doesn't! Breadth-first search is complete, meaning it'll find a solution if one exists, and given enough resources will find the optimal solution.
    Best-first search is also complete provided the heuristic (estimator of the cost/ so the prior knowledge) is admissible (meaning it overestimates the cost of getting to the solution)
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