greedy best first search What heuristic functions transform greedy best-first search into breadth-first search, depth-first search, and uniform-cost search?
 A: Your question is rather sparse on details, and is a little odd since greedy best-first search (GBFS) implies some cost of a path, unlike BFS and DFS.
Sometimes a distance to goal estimate is also known.
(Also, the CS stack exchange might be more appropriate for questions like this, though I think there is reasonable space for overlap).
In any case, GBFS has a heuristic function $h(n)$ for any given node $n$ that determines the order in which they are checked/queued. $h$ controls the order of insertion into the (priority) queue to check, in other words.
Lets start at node $s$.
For BFS, $h(n)$ should be less based on the depth of the search tree from $s$. More precisely, when you process a node $n$, all its children should be added to the queue with priority $h(n)+1$.
For DFS, it's the opposite. Add the children, but assign $h(n)-1$ instead. As you get deeper, the latest seen nodes will have the highest priority.
For uniform cost search, we are essentially trying to do Dijkstra's algorithm for a single goal node. Assuming an edge-weighted graph, use the same heuristic as BFS, except add the edge weight instead of $1$.
