# Tag Info

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In the case of UCS, the evaluation function (that is, the function that is used to select the next node to expand) is $f(n) = g(n)$, where $g(n)$ is the cost of the path from the initial node to $n$, while in the case of the greedy BFS it is $f(n) = h(n)$, where $h(n)$ is the heuristic function that estimates the cost of the path from $n$ to the goal node. ...

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Let's see their definition first: Best First Search (BFS): ‌ Best-first search is a search algorithm that explores a graph by expanding the most promising node chosen according to a specified rule. estimating the promise of node n by a "heuristic evaluation function $f(n)$ which, in general, may depend on the description of n, the ...

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Best-first search BFS is a search approach and not just a single algorithm, so there are many best-first (BFS) algorithms, such as greedy BFS, A* and B*. BFS algorithms are informed search algorithms, as opposed to uninformed search algorithms (such as breadth-first search, depth-first search, etc.), i.e. BFS algorithms make use of domain knowledge that can ...

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This is probably more easily understood as the collapse/restore macro. The idea is that the previously explored state was collapsed and only the minimum f-cost from the sub-tree was stored. This represents the best unexpanded state in the subtree that was collapsed. When restoring the portion of the collapsed tree, the f-cost of the restored node could ...

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I was struggling with the same question. This is what I came up with after thinking it through. With depth-first-search, you backtrack to a node that is a non-expanded child of your parent (or the parent of the parent when your parent has no more non-expanded children (and so on going up the tree)). So the space complexity is limited by your ancestors and ...

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