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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.
MTD(f) is an alpha-beta game tree search algorithm modified to use ‘zero-window’ initial search bounds, and memory (usually a transposition table) to reuse intermediate search results. MTD(f) is a shortened form of MTD(n,f) which stands for Memory-enhanced Test Driver with node ‘n’ and value ‘f’. [ 1 ]
Animated example of a depth-first search For the following graph: a depth-first search starting at the node A, assuming that the left edges in the shown graph are chosen before right edges, and assuming the search remembers previously visited nodes and will not repeat them (since this is a small graph), will visit the nodes in the following ...
State space search is a process used in the field of computer science, including artificial intelligence (AI), in which successive configurations or states of an instance are considered, with the intention of finding a goal state with the desired property.
Alpha–beta search can be made even faster by considering only a narrow search window (generally determined by guesswork based on experience). This is known as an aspiration window . In the extreme case, the search is performed with alpha and beta equal; a technique known as zero-window search , null-window search , or scout search .
A transposition table is a cache of previously seen positions, and associated evaluations, in a game tree generated by a computer game playing program. If a position recurs via a different sequence of moves, the value of the position is retrieved from the table, avoiding re-searching the game tree below that position.
Iterated Local Search is based on building a sequence of locally optimal solutions by: perturbing the current local minimum; applying local search after starting from the modified solution. The perturbation strength has to be sufficient to lead the trajectory to a different attraction basin leading to a different local optimum.