03_InformedSearch - 9/11/2009 Informed Methods: Heuristic...

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9/11/2009 1 Informed Search Informed Methods: Heuristic Search Idea: Informed search by using problem specific knowledge. Best First Search : Nodes are selected for expansion based on an evaluation function , f ( n ). Traditionally, f is a cost measure. Heuristic: Problem specific knowledge that (tries to) lead the search algorithm faster towards a goal state. Often implemented via heuristic function h(n). Heuristic search is an attempt to search the most promising paths first. Uses heuristics, or rules of thumb, to find the best node to expand next. Generic Best First Search 1. Set L to be the initial node(s) representing the initial state(s). 2. If L is empty, fail. Let n be the node on L that is ``most promising'‘ according to f . Remove n from L . 3. If n is a goal node, stop and return it (and the path from the initial node to n ). 4. Otherwise, add successors ( n ) to L . Return to step 2. Greedy Best First Search Heuristic function h(n): estimated cost from node n to nearest goal node. Greedy Search : Let f(n) = h(n). Example : 8 puzzle 4 4 5 6 1 732 8 2 1 8 3 765 Start State Goal State Which move is better? Heuristic: # of incorrect tiles A: Move space Left B: Move space Down C: Both the same Which move is better? Heuristic: Manhattan distance of tiles from correct location A: Move space Left B: Move space Down C: Both the same
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9/11/2009 2 1. h C = number of misplaced tiles 2. h M = Manhattan distance 8 Puzzle Which one should we use? h C h M h* Search Costs on 8 Puzzle h1 : number of misplaced tiles h2 : Manhattan distance Route Planning Greedy Best first search Straight line distances to Bucharest Route Planning Greedy Best first search 380 374 366 253 176 374 329 193 160 242 100 244 241 77 0 80 Blue –Straight line distance to Bucharest A* Search Idea
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03_InformedSearch - 9/11/2009 Informed Methods: Heuristic...

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