“If I Only had a Brain” Search

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Presentation transcript:

“If I Only had a Brain” Search Lecture 3-1 January 19th, 1999 CS250 Lecture 3-1 CS250: Intro to AI/Lisp

Blind Search No information except Initial state Operators Goal test If we want worst-case optimality, need exponential time Lecture 3-1 CS250: Intro to AI/Lisp

“How long ‘til we get there?” Add a notion of progress to search Not just the cost to date How far we have to go Lecture 3-1 CS250: Intro to AI/Lisp

Best-First Search Next node in General-Search Queuing function Replace with evaluation function Go with the most desirable path Lecture 3-1 CS250: Intro to AI/Lisp

Heuristic Functions Estimate with a heuristic function, h(n) Problem specific (Why?) Information about getting to the goal Not just where we’ve been Examples Route-finding? 8 Puzzle? Lecture 3-1 CS250: Intro to AI/Lisp

Greedy Searching Take the path that looks the best right now Lowest estimated cost Not optimal Not complete Complexity? Time: O(bm) Space: O(bm) Lecture 3-1 CS250: Intro to AI/Lisp

Best of Both Worlds? Greedy Uniform cost Minimizes total estimated cost to goal, h(n) Not optimal Not complete Uniform cost Minimizes cost so far, g(n) Optimal & complete Inefficient Lecture 3-1 CS250: Intro to AI/Lisp

Greedy + Uniform Cost Evaluate with both criteria f(n) = g(n) + h(n) What does this mean? Sounds good, but is it: Complete? Optimal? Lecture 3-1 CS250: Intro to AI/Lisp

Admissible Heuristics Optimistic: Never overestimate the cost of reaching the goal A* Search = Best-first + Admissible h(n) Lecture 3-1 CS250: Intro to AI/Lisp

A* Search Complete Optimal, if: Heuristic is admissible Lecture 3-1 CS250: Intro to AI/Lisp