ai-lect5 - Recall: breadth-first search, step by step 1...

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1 Recall: breadth-first search, step by step
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2 Implementation of search algorithms Function  General-Search(problem, Queuing-Fn)  returns  a solution, or failure nodes   make-queue(make-node(initial-state[problem])) loop do if  nodes is empty  then return  failure node   Remove-Front(nodes) if  Goal-Test[problem] applied to State(node) succeeds  then return  node nodes   Queuing-Fn(nodes, Expand(node, Operators[problem])) end Queuing-Fn( queue elements )  is a queuing function that inserts a set of  elements into the queue and determines the order of node expansion .   Varieties of the queuing function produce varieties of the search algorithm.
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3 Recall: breath-first search, step by step
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4 Breadth-first search Node queue: initialization # state depth path cost parent # 1 Arad 0 0 --
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5 Breadth-first search Node queue: add successors to queue end; empty queue from top # state depth path cost parent # 1 Arad 0 0 -- 2 Zerind 1 1 1 3 Sibiu 1 1 1 4 Timisoara 1 1 1
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6 Breadth-first search Node queue: add successors to queue end; empty queue from top # state depth path cost parent # 1 Arad 0 0 -- 2 Zerind 1 1 1 3 Sibiu 1 1 1 4 Timisoara 1 1 1 5 Arad 2 2 2 6 Oradea 2 2 2 (get smart: e.g., avoid repeated states like node #5)
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7 Depth-first search
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8 Depth-first search Node queue: initialization # state depth path cost parent # 1 Arad 0 0 --
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9 Depth-first search Node queue: add successors to queue front; empty queue from top # state depth path cost parent # 2 Zerind 1 1 1 3 Sibiu 1 1 1 4 Timisoara 1 1 1 1 Arad 0 0 --
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10 Depth-first search Node queue: add successors to queue front; empty queue from top # state depth path cost parent # 5 Arad 2 2 2 6 Oradea 2 2 2 2 Zerind 1 1 1 3 Sibiu 1 1 1 4 Timisoara 1 1 1 1 Arad 0 0 --
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11 Last time: search strategies Uninformed:  Use only information available in the problem formulation Breadth-first Uniform-cost Depth-first Depth-limited Iterative deepening Informed:  Use heuristics to guide the search Best first: Greedy search A* search
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12 Last time: search strategies Uninformed:  Use only information available in the problem formulation Breadth-first Uniform-cost Depth-first Depth-limited Iterative deepening Informed:  Use heuristics to guide the search Best first: Greedy search  --  queue first nodes that maximize heuristic “desirability”  based on estimated path cost from current node to goal; A* search –  queue first nodes that minimize sum of path cost so far and  estimated path cost to goal.
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13 This time Iterative improvement Hill climbing Simulated annealing
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ai-lect5 - Recall: breadth-first search, step by step 1...

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