2003
Learning Representative Patterns From Real Chess Positions: A Case Study
ŽIŽKA, Jan and Michal MÁDRBasic information
Original name
Learning Representative Patterns From Real Chess Positions: A Case Study
Authors
ŽIŽKA, Jan (203 Czech Republic, guarantor) and Michal MÁDR (203 Czech Republic)
Edition
Hyderabad, India, Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03), p. 1374-1387, 14 pp. 2003
Publisher
IICAI-03
Other information
Language
English
Type of outcome
Proceedings paper
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
India
Confidentiality degree
is not subject to a state or trade secret
RIV identification code
RIV/00216224:14330/03:00009152
Organization unit
Faculty of Informatics
ISBN
0-9727412-0-8
Keywords in English
pattern recognition; decision trees; classification; representation of examples; relevant attributes
Tags
Changed: 8/9/2004 16:36, doc. Ing. Jan Žižka, CSc.
Abstract
V originále
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).
Links
| MSM 143300003, plan (intention) |
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