Lecture-03-04-Uninformed_Search

# Cs561 lectures 3 4 macskassy fall 2010 comparing

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CS561 - Lectures 3-4 - Macskassy - Fall 2010

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Comparing uninformed search strategies b max branching factor of the search tree d depth of the least-cost solution m max depth of the state-space (may be infinity) l depth cutoff Criterion Breadth- first Uniform cost Depth- first Depth- limited Iterative deepening Bidirectional (if applicable) Time Space bm bl bd Optimal? Yes Yes No No Yes Yes Complete? Yes Yes No Yes if l d Yes Yes / * C b / * C b 1 d b 1 d b m b d b l b ) 2 / ( d b ) 2 / ( d b CS561 - Lectures 3-4 - Macskassy - Fall 2010
Repeated States Failure to detect repeated states can turn a linear problem into an exponential one! CS561 - Lectures 3-4 - Macskassy - Fall 2010

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Graph Search CS561 - Lectures 3-4 - Macskassy - Fall 2010
Graph Search In BFS we shouldn’t bother expanding the filled-out nodes (why?) CS561 - Lectures 3-4 - Macskassy - Fall 2010 s d e p q h r f c G a p q q b a c a e h r f c G a p q q

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Summary Problem formulation usually requires abstracting away real- world details to define a state space that can be explored using computer algorithms. Once problem is formulated in abstract form, complexity analysis helps us picking out best algorithm to solve problem. Variety of uninformed search strategies; difference lies in method used to pick node that will be further expanded . Iterative deepening search only uses linear space and not much more time than other uniformed search strategies. Graph search can be exponentially more efficient than tree search. CS561 - Lectures 3-4 - Macskassy - Fall 2010
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• Fall '09
• Artificial Intelligence, Depth-first search, Search algorithms, Search algorithm, Graph algorithms, Uniform-Cost Search

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