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@inproceedings{490371,
author = {Žižka, Jan and Mádr, Michal},
address = {Hyderabad, India},
booktitle = {Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)},
keywords = {pattern recognition; decision trees; classification; representation of examples; relevant attributes},
language = {eng},
location = {Hyderabad, India},
isbn = {0-9727412-0-8},
pages = {1374-1387},
publisher = {IICAI-03},
title = {Learning Representative Patterns From Real Chess Positions: A Case Study},
year = {2003}
}
TY - JOUR
ID - 490371
AU - Žižka, Jan - Mádr, Michal
PY - 2003
TI - Learning Representative Patterns From Real Chess Positions: A Case Study
PB - IICAI-03
CY - Hyderabad, India
SN - 0972741208
KW - pattern recognition
KW - decision trees
KW - classification
KW - representation of examples
KW - relevant attributes
N2 - 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).
ER -
ŽIŽKA, Jan a Michal MÁDR. Learning Representative Patterns From Real Chess Positions: A Case Study. In \textit{Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)}. Hyderabad, India: IICAI-03, 2003, s.~1374-1387. ISBN~0-9727412-0-8.
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