03_InformedSearch

# 03_InformedSearch - Informed Search Combinatorial Explosion...

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Informed Search

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Combinatorial Explosion Depth Nodes Time Memory 2 1100 .11 sec 1 meg 4 111,100 11 sec 106 meg 6 10 7 19 min 10 gig 8 10 9 31 hrs 1 tera 10 10 11 129 days 101 tera 12 10 13 35 yrs 10 peta 14 10 15 3523 yrs 1 exa Rely only on problem description
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.

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Uniform-cost search is NOT heuristic search s s s s 0 A A A A B B B C C C C G G G G 1 5 5 5 5 5 15 15 15 15 11 11 10 s 1 B 10 Requirement: g (Successor( n )) g ( n ) It only looks backwards; has no ability to predict future costs. Always expand lowest cost node in open-list. Goal-test only before expansion, not after generation.
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. seemingly-best-first. ..

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A good heuristic Heuristic cost should never overestimate the actual cost of a node I.e. it must be “optimistic” So that we never overlook a node that is actually good
Heuristic function h(n): estimated cost from node n to nearest goal node. Greedy Search

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03_InformedSearch - Informed Search Combinatorial Explosion...

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