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  2. Best-first search - Wikipedia

    en.wikipedia.org/wiki/Best-first_search

    Best-first search is a class of search algorithms which explores a graph by expanding the most promising node chosen according to a specified rule.. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function () which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to ...

  3. Iterative deepening A* - Wikipedia

    en.wikipedia.org/wiki/Iterative_deepening_A*

    It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the remaining cost to get to the goal from the A* search algorithm. Since it is a depth-first search algorithm, its memory usage is lower than in A*, but unlike ordinary iterative deepening search, it ...

  4. A* search algorithm - Wikipedia

    en.wikipedia.org/wiki/A*_search_algorithm

    A* is an informed search algorithm, or a best-first search, meaning that it is formulated in terms of weighted graphs: starting from a specific starting node of a graph, it aims to find a path to the given goal node having the smallest cost (least distance travelled, shortest time, etc.).

  5. Heuristic (computer science) - Wikipedia

    en.wikipedia.org/wiki/Heuristic_(computer_science)

    In such search problems, a heuristic can be used to try good choices first so that bad paths can be eliminated early (see alpha–beta pruning). In the case of best-first search algorithms, such as A* search, the heuristic improves the algorithm's convergence while maintaining its correctness as long as the heuristic is admissible.

  6. ID3 algorithm - Wikipedia

    en.wikipedia.org/wiki/ID3_algorithm

    It uses a greedy strategy by selecting the locally best attribute to split the dataset on each iteration. The algorithm's optimality can be improved by using backtracking during the search for the optimal decision tree at the cost of possibly taking longer. ID3 can overfit the training data. To avoid overfitting, smaller decision trees should ...

  7. State space search - Wikipedia

    en.wikipedia.org/wiki/State_space_search

    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. Problems are often modelled as a state space, a set of states that a problem can be in.

  8. Tabu search - Wikipedia

    en.wikipedia.org/wiki/Tabu_search

    Tabu search (TS) is a metaheuristic search method employing local search methods used for mathematical optimization. It was created by Fred W. Glover in 1986 [ 1 ] and formalized in 1989. [ 2 ] [ 3 ]

  9. Outline of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Outline_of_artificial...

    Recursive self improvement (aka seed AI) – speculative ability of strong artificial intelligence to reprogram itself to make itself even more intelligent. The more intelligent it got, the more capable it would be of further improving itself, in successively more rapid iterations, potentially resulting in an intelligence explosion leading to ...