Lecture-5

Lecture-5 - Artificial Intelligence CS 165A 165A Tuesday,...

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Artificial Intelligence S 165A CS 165A Tuesday, Jan 18, 2011 formed earch (Ch Informed Search (Ch. 3) 1
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Notes • Lecture notes – Username: cs165a – Password: winter2011 2
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Review • Uninformed Search – Breadth First –D e p t h F i r s t – Uniform Cost epth Limited – Depth Limited – Iterative Deepening – Bidirectional • Informed Search –H e u r i s t i c s dmissible Admissible – Best First Search Greedy 3 A*
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Best-First Search function B EST -F IRST -S EARCH ( problem, E VAL -F N ) returns a solution or failure Q UEUING -F N a function that orders nodes by E -F N return G ENERAL -S EARCH ( problem, Q UEUING -F N ) But what evaluation function to use? 4
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Greedy Best-First Search Uses a heuristic function, h(n) , as the E VAL -F N h(n) estimates of the cost of the best path from state n to a goal state h(goal) = 0 Greedy search – always expand the node that appears to be the closest the goal (i e with the smallest to the goal (i.e., with the smallest h ) – Instant gratification, hence “greedy” function G REEDY -S EARCH ( problem, h ) returns a solution or failure return B EST -F IRST -S EARCH ( problem, h ) Greedy search often performs well – It doesn’t always find the best solution – It may get stuck 5 – It depends on the particular h function
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Romania (cont.) 6
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“A” Search • Uniform-cost search minimizes g(n) (“past” cost) • Greedy search minimizes h(n) (“expected” or “future” cost) “A Search” combines the two: – Minimize f(n) = g(n) + h(n) t f t h t d t h f t – Accounts for the “past” and the “future” – Estimates the cheapest solution (complete path) through node n 7
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A* Search •“ A* Search” is A Search with an admissible h h
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Lecture-5 - Artificial Intelligence CS 165A 165A Tuesday,...

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