Iterative deepening depth-first search
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Iterative deepening depth-first search or IDDFS is a state space search strategy in which a depth-limited search is run repeatedly, increasing the depth limit with each iteration until it reaches d, the depth of the shallowest goal state. On each iteration, the IDDFS visits the nodes in the search tree in the same order as depth-first search, but the cumulative order in which nodes are first visited, assuming no pruning, is effectively breadth-first.
Iterative deepening depth-first search combines depth-first search's space-efficiency and both breadth-first search's completeness (when the branching factor is finite) and optimality (when the path cost is a non-decreasing function of the depth of the node).
The space complexity of IDDFS is O(bd), where b is the branching factor. Since iterative deepening visits states multiple times it may seem wasteful, but it turns out to be not so costly, since in a tree most of the nodes are in the bottom level, so it does not matter much if the upper levels are visited multiple times.
The main advantage of this algorithm in game tree searching is that the earlier searches tend to improve the commonly used heuristics, such as the killer heuristic and alpha-beta pruning, so that a more accurate estimate of the score of various nodes at the final depth search can occur, and the search completes more quickly since it is done in a better order (it can be shown that alpha-beta pruning works much better if the moves are generated with the moves likely to be the best moves evaluated first).
In fact the time complexity of IDDFS works out to be the same as Depth-first search — O(bd).
Similar to iterative deepening is a search strategy called iterative lengthening search that works with increasing path-cost limits instead of depth-limits. It turns out however, that iterative lengthening incurs substantial overhead that make it less useful than iterative deepening as a search strategy.
