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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01gq67jt61b
Title: CPYu: An Optimization of Chess Playing Through Game Tree Search Reduction and Supervised Learning
Authors: Yu, Sally
Advisors: Kpotufe, Samory
Department: Operations Research and Financial Engineering
Class Year: 2016
Abstract: It is infeasible to calculate all the different move sequences in a chess game, since the game tree is far too large for even modern day computers. CPYu, a chess engine implemented in Python and built from scratch, was created to ana- lyze the efficacy of game tree pruning algorithms and supervised learning meth- ods. The pruning algorithms, which induce game tree truncation and search space reduction, result in significant decreases in computation time. Supervised learning on grandmaster chess games was used to train CPYus evaluation of a position and increase its playing strength.
Extent: 86 pages
URI: http://arks.princeton.edu/ark:/88435/dsp01gq67jt61b
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2016

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