iterative deepening การใช้

• Given a random cube C, it is solved as iterative deepening.
• But iterative lengthening incurs substantial overhead that makes it less useful than iterative deepening.
• This means that the time complexity of iterative deepening is still O ( b ^ d ).
• Using iterative deepening for fair enumeration, each arbitrary but fixed sentence and its corresponding AST will be generated eventually:
• Similar to iterative deepening is a search strategy called iterative lengthening search that works with increasing path-cost limits instead of depth-limits.
• When an appropriate depth limit is not known a priori, iterative deepening depth-first search applies DFS repeatedly with a sequence of increasing limits.
• Of course there is no way of knowing the best move, but when iterative deepening is used, the move which was found to be the best in a shallower search is a good approximation.
• Since the minimax algorithm and its variants are inherently iterative deepening is usually used in conjunction with alpha beta so that a reasonably good move can be returned even if the algorithm is interrupted before it has finished execution.
• The higher the branching factor, the lower the overhead of repeatedly expanded states, but even when the branching factor is 2, iterative deepening search only takes about twice as long as a complete breadth-first search.
• Hence, there are implementations of Curry, like KiCS2, where the user can easily select a search strategy, like depth-first search ( backtracking ), breadth-first search, iterative deepening, or parallel search.
• Another advantage of using iterative deepening is that searches at shallower depths give move-ordering hints, as well as shallow alpha and beta estimates, that both can help produce cutoffs for higher depth searches much earlier than would otherwise be possible.
• All together, an iterative deepening search from depth 1 all the way down to depth d expands only about 11 \ % more nodes than a single breadth-first or depth-limited search to depth d, when b = 10.
• A method that may visit some nodes more than once but works in polynomial space is to visit in a depth-first manner with iterative deepening : one first visits the tree up to a certain depth, then increases the depth and perform the visit again.
• In the artificial intelligence mode of analysis, with a branching factor greater than one, iterative deepening increases the running time by only a constant factor over the case in which the correct depth limit is known due to the geometric growth of the number of nodes per level.
• 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.
• It looks like GNU aspell converts words into a phonetic representation called " soundslike " and then does iterative deepening edit distance on that, with an index for small edit distances ( 1 and 2 ) .-- talk ) 15 : 18, 30 January 2008 ( UTC)
• In computer science, "'iterative deepening search "'or more specifically "'iterative deepening depth-first search "'( IDS or IDDFS ) is a nodes in the search tree in the same order as depth-first search, but the cumulative order in which nodes are first visited is effectively breadth-first.
• In computer science, "'iterative deepening search "'or more specifically "'iterative deepening depth-first search "'( IDS or IDDFS ) is a nodes in the search tree in the same order as depth-first search, but the cumulative order in which nodes are first visited is effectively breadth-first.
• While the standard iterative deepening depth-first search uses search depth as the cutoff for each iteration, the IDA * uses the more informative f ( n ) = g ( n ) + h ( n ), where g ( n ) is the cost to travel from the root to node n and h ( n ) is a problem-specific heuristic estimate of the cost to travel from n to the solution.