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  2. Iterative deepening A* - Wikipedia

    en.wikipedia.org/wiki/Iterative_deepening_A*

    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 ...

  3. Iterative deepening depth-first search - Wikipedia

    en.wikipedia.org/wiki/Iterative_deepening_depth...

    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.

  4. MTD(f) - Wikipedia

    en.wikipedia.org/wiki/MTD(f)

    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 ]

  5. Fringe search - Wikipedia

    en.wikipedia.org/wiki/Fringe_search

    In essence, fringe search is a middle ground between A* and the iterative deepening A* variant (IDA*). If g(x) is the cost of the search path from the first node to the current, and h(x) is the heuristic estimate of the cost from the current node to the goal, then ƒ(x) = g(x) + h(x), and h* is the actual path cost to the goal.

  6. A* search algorithm - Wikipedia

    en.wikipedia.org/wiki/A*_search_algorithm

    An example of an A* algorithm in action where nodes are cities connected with roads and h(x) is the straight-line distance to the target point: Key: green: start; blue: goal; orange: visited The A* algorithm has real-world applications.

  7. Optimal solutions for the Rubik's Cube - Wikipedia

    en.wikipedia.org/wiki/Optimal_solutions_for_the...

    To solve this problem, Kociemba devised a lookup table that provides an exact heuristic for . [18] When the exact number of moves needed to reach G 1 {\displaystyle G_{1}} is available, the search becomes virtually instantaneous: one need only generate 18 cube states for each of the 12 moves and choose the one with the lowest heuristic each time.

  8. Yelp's Best New Restaurants for 2024: Would you dine at one ...

    www.aol.com/yelps-best-restaurants-2024-dine...

    The restaurant features Middle Eastern dips, schmears and small plates, all meant for sharing around the table. Meesh Meesh was ranked No. 2 on Yelp's Best New Restaurants in the South for 2024 .

  9. Jump point search - Wikipedia

    en.wikipedia.org/wiki/Jump_point_search

    In computer science, jump point search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning, [1] eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied.