2003
Learning Representative Patterns From Real Chess Positions: A Case Study
ŽIŽKA, Jan a Michal MÁDRZákladní údaje
Originální název
Learning Representative Patterns From Real Chess Positions: A Case Study
Autoři
ŽIŽKA, Jan (203 Česká republika, garant) a Michal MÁDR (203 Česká republika)
Vydání
Hyderabad, India, Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03), od s. 1374-1387, 14 s. 2003
Nakladatel
IICAI-03
Další údaje
Jazyk
angličtina
Typ výsledku
Stať ve sborníku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Indie
Utajení
není předmětem státního či obchodního tajemství
Kód RIV
RIV/00216224:14330/03:00009152
Organizační jednotka
Fakulta informatiky
ISBN
0-9727412-0-8
Klíčová slova anglicky
pattern recognition; decision trees; classification; representation of examples; relevant attributes
Štítky
Změněno: 8. 9. 2004 16:36, doc. Ing. Jan Žižka, CSc.
Anotace
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).
Návaznosti
| MSM 143300003, záměr |
|