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Iterative deepening A* (IDA*) is a graph traversal and path search algorithm that can find the shortest path between a designated start node and any member of a set of goal nodes in a weighted graph. It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the ...
Iterative deepening prevents this loop and will reach the following nodes on the following depths, assuming it proceeds left-to-right as above: 0: A; 1: A, B, C, E (Iterative deepening has now seen C, when a conventional depth-first search did not.) 2: A, B, D, F, C, G, E, F (It still sees C, but that it came later.
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. DFS may also be used to collect a sample of graph nodes.
Iterative deepening depth-first search (IDDFS): a state space search strategy; Jump point search: an optimization to A* which may reduce computation time by an order of magnitude using further heuristics; Lexicographic breadth-first search (also known as Lex-BFS): a linear time algorithm for ordering the vertices of a graph
Iterative lengthening search - referred to in Iterative deepening depth-first search; Jump string - somehow related to jump tables; Lasserre Hierarchy - hierarchy of semidefinite programming relaxations; March Algorithm - A class of algorithms used to test a computing device's memory. It consists of a sequence of operations applied to each ...
The space complexity of A* is roughly the same as that of all other graph search algorithms, as it keeps all generated nodes in memory. [1] In practice, this turns out to be the biggest drawback of the A* search, leading to the development of memory-bounded heuristic searches, such as Iterative deepening A*, memory-bounded A*, and SMA*.
A related method, called progressive bias, consists in adding to the UCB1 formula a element, where b i is a heuristic score of the i-th move. [ 37 ] The basic Monte Carlo tree search collects enough information to find the most promising moves only after many rounds; until then its moves are essentially random.
In iterative deepening search, the previous iteration has already established a candidate for such a sequence, which is also commonly called the principal variation. For any non-leaf in this principal variation, its children are reordered such that the next node from this principal variation is the first child.