Heuristic Search Russell and Norvig: Chapter 4 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm.
HEURISTIC SEARCH Ivan Bratko Faculty of Computer and Information Sc. University of Ljubljana.
Artificial Intelligence Chapter 9 Heuristic Search Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.
Optimality of A*(standard proof) Suppose suboptimal goal G 2 in the queue. Let n be an unexpanded node on a shortest path to optimal goal G. f(G 2 ) =
A* Search Uses evaluation function f (n)= g(n) + h(n) where n is a node. – g is a cost function Total cost incurred so far from initial state at node n.
A* and D* Search Kevin Tantisevi. Outline Overview of Search Techniques A* Search D* Search.
8/29. Administrative.. Bouncing mails –Qle01; jmussem; rbalakr2 Send me a working email address for class list Blog posting issues Recitation session.
9/9. Num iterations: (d+1) Asymptotic ratio of # nodes expanded by IDDFS vs DFS (b+1)/ (b-1) (approximates to 1 when b is large)
3/3 Factoid for the day: “Most people have more than the average number of feet” & eyes & ears & noses.
1 search CS 331/531 Dr M M Awais A* Examples:. 2 search CS 331/531 Dr M M Awais 8-Puzzle 0+41+5 1+3 3+3 3+4 3+24+15+2 5+0 2+3 2+4 2+3 f(N) = g(N) + h(N)
1 Review Best-first search uses an evaluation function f(n) to select the next node for expansion. Greedy best-first search uses f(n) = h(n). Greedy best.
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