0411134 20240103182304.420060210235959.9 Florence University of Florence 2003 7 s. 132-138MarinaiS.GoriM. probabilistic neural networks finite mixtures EM algorithm cav_un_auth*0101091 Grim Jiří UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101197 Somol Petr UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0213065 Just P. CZ 12B 06D GA402/01/0981 GA ČR cav_un_auth*0008962 GA402/03/1310 GA ČR cav_un_auth*0009030 KSK1019101 GA AV ČR cav_un_auth*0000219 CEZ:AV0Z1075907 The goal of the paper is to design a probabilistic neural network playing a simple two-player game "Tic-Tac-Toe". The game is considered as a problem of a repeating evaluation of preferences of possible moves. Assuming the probabilistic neural network in the role of the evaluation function we can solve the problem by estimating the probability distribution of advantageous moves. The unknown distribution is estimated in the form of a finite discrete mixture of product components by means of EM algorithm. cav_un_auth*0213066 IAPR TC3 Workshop 2003 /1./ Florence IT 12.09.2003-13.09.2003 BB RO http://hdl.handle.net/11104/0131221 UTIA-B 20030121 2003 2003 Florence University of Florence Artificial Neural Networks in Pattern Recognition. Proceedings 132 138 Marinai S. 340 Gori M. 340