Best-First Search Algorithm

From GM-RKB
Jump to navigation Jump to search

A Best-First Search Algorithm is a heuristic search algorithm that explores a graph by expanding the most promising node chosen according to a specified rule.



References

2012

  • http://en.wikipedia.org/wiki/Best-first_search
    • 'Best-first search is a search algorithm 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 [math]\displaystyle{ f(n) }[/math] which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to that point, and most important, on any extra knowledge about the problem domain."[1] [2]

      Some authors have used "best-first search" to refer specifically to a search with a heuristic that attempts to predict how close the end of a path is to a solution, so that paths which are judged to be closer to a solution are extended first. This specific type of search is called greedy best-first search.

      Efficient selection of the current best candidate for extension is typically implemented using a priority queue.

      The A* search algorithm is an example of best-first search. Best-first algorithms are often used for path finding in combinatorial search.

  1. Pearl, J. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, 1984. p. 48.
  2. Template:Russell Norvig 2003. pp. 94 and 95 (note 3).


OPEN = [initial state] CLOSED = [] while OPEN is not empty do

1. Remove the best node from OPEN, call it n, add it to CLOSED.
2. If n is the goal state, backtrace path to n (through recorded parents) and return path.
3. Create n's successors.
4. For each successor do:
      a. If it is not in CLOSED: evaluate it, add it to OPEN, and record its parent.
      b. Otherwise: change recorded parent if this new path is better than previous one.

done </source>