Breadth-first search vs Depth first search in knowledge-based chess engines I would like to learn about the difference between depth-first and breadth-first search in knowledge-based chess engines (that, of course, excludes alpha-zero). The difference isn't that clear-cut, but, to my knowledge, some engines prefer to go deeper than explore more options per move. It seems that depth-first search, as it is used in Stockfish, has prevailed for now, but I don't necessarily find that obvious. Can anyone point me to research which has been done in this area?
Strength in chess engines depends on speed and speed depends on how strongly you can prune the tree. That is, how many nodes you can discard without degrading the accuracy of your search result. 

In principle you might be able to visit the same nodes using either BFS or DFS, so a priori it is not clear which would be better prunable i.e. faster. 

In chess, however, DFS is better prunable because one strong countermove is enough to invalidate a move. Therefore, the value of a move depends more strongly on a small set of next countermoves and the value of these countermoves on a small set of next moves etc. So, Min-Max-Search naturally lends itself to better pruning with DFS.