# A* Pathfinding for Beginners

By Patrick Lester (Updated July 18, 2005) This article has been translated into Albanian, Chinese, Finnish, German, Greek, Korean, Polish, Portuguese, Romanian, Russian, Serbian, and Spanish. Other translations are welcome. See email address at the bottom of this article. The A* (pronounced A-star) algorithm can be complicated for beginners. This article does not try to be the definitive work on the subject. Finally, this article is not program-specific. But we are getting ahead of ourselves. Introduction: The Search Area Let’s assume that we have someone who wants to get from point A to point B. [Figure 1] The first thing you should notice is that we have divided our search area into a square grid. These center points are called “nodes”. Starting the Search Once we have simplified our search area into a manageable number of nodes, as we have done with the grid layout above, the next step is to conduct a search to find the shortest path. We begin the search by doing the following: where 1.
Step by Step: Alpha Beta Pruning
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A* search
Sorting Algorithm Animations
Algorithms in Java, Parts 1-4, 3rd edition by Robert Sedgewick. Addison Wesley, 2003. Quicksort is Optimal by Robert Sedgewick and Jon Bentley, Knuthfest, Stanford University, January, 2002. Dual Pivot Quicksort: Code by Discussion. Bubble-sort with Hungarian (“Csángó”) folk dance YouTube video, created at Sapientia University, Tirgu Mures (Marosvásárhely), Romania. Select-sort with Gypsy folk dance YouTube video, created at Sapientia University, Tirgu Mures (Marosvásárhely), Romania. Sorting Out Sorting, Ronald M.
Java tree data-structure
Sorting and Searching Strings
Ternary Search Trees Jon Bentley and Robert Sedgewick feature article in Dr. Abstract When you have to store a set of strings, what data structure do you use? Fast Algorithms for Sorting and Searching Strings presented at Eighth Annual ACM-SIAM Symposium on Discrete Algorithms New Orleans, January, 1997 We present theoretical algorithms for sorting and searching multidimensional data and practical C implementations for the application where keys are character strings. Comments, questions, suggestions: mail rs@cs.princeton.edu mail jlb@research.bell-labs.com

Alpha–beta pruning
History[edit] Improvements over naive minimax[edit] An illustration of alpha–beta pruning. The benefit of alpha–beta pruning lies in the fact that branches of the search tree can be eliminated. . An animated pedagogical example that attempts to be human-friendly by substituting initial infinite (or arbitrarily large) values for emptiness and by avoiding using the negamax coding simplifications. Normally during alpha–beta, the subtrees are temporarily dominated by either a first player advantage (when many first player moves are good, and at each search depth the first move checked by the first player is adequate, but all second player responses are required to try to find a refutation), or vice versa. The algorithm maintains two values, alpha and beta, which represent the maximum score that the maximizing player is assured of and the minimum score that the minimizing player is assured of respectively. Pseudocode[edit] Heuristic improvements[edit] Other algorithms[edit] See also[edit]

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