Publication details

Learning Representative Patterns From Real Chess Positions: A Case Study



Year of publication 2003
Type Article in Proceedings
Conference Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords pattern recognition; decision trees; classification; representation of examples; relevant attributes
Description This paper deals with a particular pattern recognition by machine learning. The patterns are specific chess positions. A computer learns if a special pattern leads to a winning or losing game, i.e., a classification task based on real results and examples. As a learning algorithm, decision trees generated by the program C5/See5, also with boosting, were used. This algorithm does not employ chess rules or calculations of positions, it just learns from a selected set of 450 training positive and negative examples with 8 different representations of real positions played by human players. The most accurate classification reaches 92.98% for the combination of automatically generated trivial descriptions of positions (64 attributes) with expert descriptions suggested by humans (92 attributes).
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