Abstract
A refinement of minimax <145> to determine the optimal move in a game. Nodes that are not needed to evaluate the possible moves of the top node are ‘pruned’. Suppose that MAX is to move at parent node P. and that it is known from previous calculations that daughter D1 guarantees a minimum gain of say +20 for MAX. Now we start exploring D2 and discover that the opponent can force a maximal gain of +10 by reacting to D2 with D2. 1. In this case there is no need to explore other daughters of D2, because MAX can never gain more than +10 and therefore will always prefer D1. Following this line of reasoning, both from the point of view of MAX and of MIN, large parts of the tree need not be explored and an optimal solution will still be found.
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Reference
Barr. A. and Feigenbaum. E. A. (editors). The Handbook of Artificial Intelligence Vol. 1. Kaufmann. 1981.
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© 1984 Springer-Verlag Berlin Heidelberg
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Bundy, A., Wallen, L. (1984). Alpha/Beta Pruning. In: Bundy, A., Wallen, L. (eds) Catalogue of Artificial Intelligence Tools. Symbolic Computation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-96868-6_7
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DOI: https://doi.org/10.1007/978-3-642-96868-6_7
Publisher Name: Springer, Berlin, Heidelberg
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