Detailed Information on Publication Record
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
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
India
Confidentiality degree
není předmětem státního či obchodního tajemství
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
Změněno: 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) |
|