A-Star Algorithm

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Heuristic Search ‣ Heuristic Search Tree – Use total x-/ydistance (Manhattan distance) heuristic – Explore the lowest scored states © Copyright 2013 Brent Nash & Mark Redekopp, All Rights Reserved H=6 1 2 4 8 3 7 6 5 H=7 1 2 H=5 4 8 3 7 6 5 1 2 3 4 8 7 6 5 H=6 H=6 H=4 1 2 1 2 3 1 2 3 4 8 3 4 8 4 8 5 7 6 5 7 6 5 7 6 H=5 1 2 3 4 8 7 6 5 1 2 3 4 8 5 7 6 H=3 1 2 3 4 5 7 8 6 H=2 H=1 1 2 3 4 5 7 8 6 Goal 1 2 3 4 5 6 7 8 8
Caution About Heuristics ‣ Heuristics are just estimates and thus could be wrong ‣ Sometimes pursuing lowest heuristic score leads to a less-than optimal solution or even no solution ‣ Solution – Take # of moves from start (depth) into account H=2 H=1 Goal <strong>Star</strong>t H=1 H=1 H=1 H=1 H=1 H=1 … © Copyright 2013 Brent Nash & Mark Redekopp, All Rights Reserved 9
Page 1 and 2: CSCI 102 A-Star Algorithm Mark Rede
Page 3 and 4: Search Methods ‣ Many systems req
Page 5 and 6: Search Methods ‣ Brute-Force Sear
Page 7: Heuristics ‣ Heuristics are “sc
Page 11 and 12: A-Star Algorithm ‣ Maintain 2 lis
Page 13 and 14: Path-Planning w/ A* Algorithm ‣ F
Page 27 and 28: A* and BFS ‣ BFS explores all nod

A-Star Algorithm

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