| bibtype |
J -
Journal Article
|
| ARLID |
0411404 |
| utime |
20240103182325.0 |
| mtime |
20060210235959.9 |
| title
(primary) (eng) |
Probabilistic neural network playing and learning Tic-Tac-Toe |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0257389 |
| ISSN |
0167-8655 |
| title
|
Pattern Recognition Letters |
| volume_id |
26 |
| volume |
12 (2005) |
| page_num |
1866-1873 |
| publisher |
|
|
| title
(cze) |
Pravděpodobnostní neuronová síť hrající piškvorky schopná učení |
| keyword |
neural networks |
| keyword |
distribution mixtures |
| keyword |
playing games |
| author
(primary) |
| ARLID |
cav_un_auth*0101091 |
| name1 |
Grim |
| name2 |
Jiří |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101197 |
| name1 |
Somol |
| name2 |
Petr |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101182 |
| name1 |
Pudil |
| name2 |
Pavel |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| COSATI |
09K |
| COSATI |
12B |
| cas_special |
| project |
| project_id |
GA402/02/1271 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0008983 |
|
| project |
| project_id |
GA402/03/1310 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0009030 |
|
| project |
| project_id |
FP6-507772 |
| agency |
Comission EU |
| country |
XE |
|
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0001814 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
A probabilistic neural network is applied as a tool to approximate the statistical evaluation function for a simple version of the game Tic-Tac-Toe. We solve the problem by a sequential estimation of the underlying discrete distribution mixture of product components. The training data is obtained by observing a simple artifical player based on a look-up table. The resulting neural network outperforms the artificial player both in the starting and defending position. |
| abstract
(cze) |
Pravděpodobnostní neuronová síť je použita jako nástroj pro aproximaci statistické evaluační funkce pro hru "piškvorky". Řešení spočívá v odhadu distribuce výhodných stavů ve tvaru distribuční směsi s použitím EM algoritmu. Trénovací datový soubor je získáván záznamem úspěšných tahů ze hry simulované pomocí jednoduchého algoritmu. Výsledná neuronová síť je úspěšnější než umělý algoritmus jak v zahajovací tak i v obranné pozici. |
| RIV |
BB |
| reportyear |
2006 |
| department |
RO |
| permalink |
http://hdl.handle.net/11104/0131486 |
| ID_orig |
UTIA-B 20050134 |
| arlyear |
2005 |
| mrcbU63 |
cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 26 č. 12 2005 1866 1873 Elsevier |
|