383-Fall11-Lec5 - Informed(Heuristic Search CMPSCI 383 1...

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1 CMPSCI 383 September 20, 2011 Informed (Heuristic) Search
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2 Tip for doing well Begin Assignments Early
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3 Today’s lecture Informed (Heuristic) search methods Best-First Search Greedy Best-First Search A* search Heuristic functions
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4 Review: Uninformed search strategies Breadth-first search Uniform-cost search Depth-first search Depth-limited search Iterative deepening depth-first search
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5 Review
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6 Review Breadth-first search Selects shallowest unexpanded node FIFO queue Uniform-cost search Selects node with lowest path cost Priority queue by path cost Depth-first search Selects deepest unexpanded node LIFO queue (stack) Depth-limited search Depth-first with nodes at depth limit treated as having no successors Iterative deepening depth-first search Repeated depth-limited with increasing limit until goal found Note: step costs assumed non-negative
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7 Review: How do you evaluate a search strategy? Completeness — Does it always find a solution if one exists? Optimality — Does it find the best solution? Time complexity Space complexity
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8 Informed (heuristic) search strategies Use problem-specific knowledge beyond what is given in the problem definition
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9 Best-First Search Idea : use an evaluation function f(n) For each node, gives an estimate of "desirability” Expand most desirable unexpanded node Implementation : Order the nodes in fringe in decreasing order of desirability Special cases : greedy best-first search A * search
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10 Heuristic function heuristic function h(n) — estimated cost of the cheapest path form the state at node n to a goal state. “Heuristic” “proceeding to a solution by trial and error or by rules that are only loosely defined.” * “a technique designed to solve a problem that ignores whether the solution can be proven to
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