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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. 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.).

  4. 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.

  5. 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 ...

  6. Computational learning theory - Wikipedia

    en.wikipedia.org/wiki/Computational_learning_theory

    In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms. [ 1 ] Overview

  7. 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 ...

  8. 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.

  9. Beam search - Wikipedia

    en.wikipedia.org/wiki/Beam_search

    Beam search with width 3 (animation) In computer science, beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is a modification of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states ...

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