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Artificial Intelligence: A* Pathfinding


Beyond A*

Mario Grimani (Xtreme Strategy Games), Matthew Titelbaum (Monolith Productions)
Game Programming Gems 5, 2005.

Advanced Pathfinding with Minimal Replanning Cost: Dynamic A Star (D*)

Marco Tombesi
Game Programming Gems 5, 2005.

Basic A* Pathfinding Made Simple

James Matthews (Generation5)
AI Game Programming Wisdom, 2002.

Generic A* Pathfinding

Daniel Higgins (Stainless Steel Software)
AI Game Programming Wisdom, 2002.

Pathfinding Design Architecture

Daniel Higgins (Stainless Steel Software)
AI Game Programming Wisdom, 2002.

How to Achieve Lightning Fast A*

Daniel Higgins (Stainless Steel Software)
AI Game Programming Wisdom, 2002.

Practical Optimizations for A* Path Generation

Timothy Cain (Troika Games)
AI Game Programming Wisdom, 2002.
Abstract: The A* algorithm is probably the most widely used path algorithm in games, but in its pure form, A* can use a great deal of memory and take a long time to execute. While most optimizations deal with improving the estimate heuristic or with storing and searching the open and closed lists more efficiently, this article examines methods of restricting A* to make it faster and more responsive to changing map conditions. Such A* restrictions take the form of artificially constricting the search space, using partial solutions, or short-circuiting the algorithm altogether. For each restriction, the situations in which these optimizations will prove most useful are discussed.

Tactical Path-Finding with A*

William van der Sterren (CGF-AI)
Game Programming Gems 3, 2002.

The Basics of A* for Path Planning

Bryan Stout
Game Programming Gems, 2000.

A* Aesthetic Optimizations

Steve Rabin (Nintendo of America)
Game Programming Gems, 2000.

A* Speed Optimizations

Steve Rabin (Nintendo of America)
Game Programming Gems, 2000.

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