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Heuristic (Informed) Search

1 Heuristic (Informed) Search (Wh t t h tl ) 1(Where we try to choose smartly) R&N: Chap. 4, Sect. 3 Search Algorithm #2 Search # (initial-node,FRINGE)Recall that the orderingof FRINGE defines the Search empty(FRINGE) then return REMOVE(FRINGE) STATE(N) GOAL?(s) then return path or goal every state s in SUCCESSORS(s) a node N as a successor of (N ,FRINGE)Best-First Search It exploits state descriptionto estimate how good each Search node is An evaluation functionf maps each node N of the Search tree to a real number 3f(N) 0 [Traditionally, f(N) is an estimated cost; so, the smaller f(N), the more promising N] Best-first searchsorts the FRINGE in increasing f[Arbitrary order is assumed among nodes with equal f]Best-First Search It exploits state descriptionto estimate how good each Search node is An evaluat

3(N) = sum of permutation inversions = 4 + 6 + 3 + 1 + 0 + 2 + 0 + 0 = 16 is not admissible h(N) = number of misplaced tiles = 6 8-Puzzle Heuristics 4 1 7 5 2 3 6 8 STATE 4 6 7 1 5 2 8 3 18 h 1(N) = number of misplaced tiles = 6 is admissible h 2(N) = sum of the (Manhattan) distances of every tile to its goal position = 2 + 3 + 0 + 1 + 3 + 0 ...

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