03-informed_search - Informed Methods Heuristic Search...

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1 Foundations of Artificial Intelligence Informed Search CS472 – Fall 2007 Thorsten Joachims 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 7 3 2 8 2 1 8 3 7 6 5 Start State Goal State Example: Suboptimal Best First-Search There exist strategies that enable optimal paths to be found without examining all possible paths.
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  • Summer '07
  • JOACHIMS
  • Artificial Intelligence, Search algorithms, Search algorithm, A* search algorithm, Best-first search, Admissible heuristic, Consistent heuristic

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