AI News, Artificial Intelligence/Search/Heuristic search/Best-first search

Artificial Intelligence/Search/Heuristic search/Best-first search

“Heuristic” here refers to a general problem-solving rule or set of rules that do not guarantee the best solution or even any solution, but serves as a useful guide for problem-solving.

Best-first search is a graph-based search algorithm (Dechter and Pearl, 1985), meaning that the search space can be represented as a series of nodes connected by paths.

Since all unvisited successor nodes of every visited node are included in the OPEN list, the algorithm is not restricted to only exploring successor nodes of the most recently visited node.

If any successor is the goal node, the algorithm returns success and the solution, which consists of a path traced backwards from the goal to the start node.

The particular evaluation function used to determine the score of a node is not precisely defined in the above algorithm, because the actual function used is up to the determination of the programmer, and may vary depending on the particularities of the search space.

While the evaluation function can determine to a large extent the effectiveness and efficiency of the search (Pearl, 1984), for the purposes of understanding the search algorithm we need not be concerned with the particularities of the function.

A crawler that uses best-first search generally uses an evaluation function that assigns priority to links based on how closely the contents of their parent page resemble the search query (Menczer, Pant, Ruiz, and Srinivasan, 2001). In

In such cases, the search algorithm treats each tile as a node, with the neighbouring unblocked tiles being successor nodes, and the goal node being the destination tile (Koenig, 2004).

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