20.500.12556/RUL-96741 Odkrivanje šahovskih motivov z analizo očesnih premikov šahista Discovering chess motifs with analysis of chess player's eye movement Ko šahist išče najboljšo potezo v dani poziciji, v mislih preiskuje drevo možnih nadaljevanj partije. Da lahko obvlada veliko kombinatorično zahtevnost tega drevesa, si šahist pomaga z značilnimi šahovskimi motivi, kot so dvojni napadi ali vezave figur. V tem delu poskušamo iz posnetka očesnih premikov šahista med reševanjem problema avtomatsko detektirati motive, ki jih je šahist uporabljal med reševanjem. Razvili smo formulo, ki podatke, pridobljene s sledenjem očesnim premikom med reševanjem problema, pretvori v pripadnosti vnaprej določenim šahovskim motivom, ki se nahajajo v poziciji. Rezultate smo analizirali najprej ročno, ter jih primerjali z retrospekcijami šahistov, izvedenimi po opravljenem poskusu. Nato smo časovne vrste pripadnosti motivom na različne načine prilagodili potrebam strojnega učenja, ter z uporabo nevronske mreže napovedali šahistove odgovore in napovedi ovrednotili. Razvita metoda za določanje pripadnosti motivom deluje zadovoljivo, ni pa odporna na primere, kjer obstaja visoka stopnja medsebojnega prepletanja motivov. When looking for a best move in a given position, a chess player explores in his mind a tree of possible continuations of the game. To cope with a large combinatorial complexity of this tree, the player uses typical chess motifs, such as double attacks or pinned pieces. In this thesis we attempt to automatically detect from the player's eye movement the motifs that the player is using during problem solving. We developed a formula that converts eye tracking data obtained from problem solving, into a degree of membership for predefined chess motifs in the position. Results were analysed and compared with retrospections of chess players, which were obtained immediately after the problem solving experiment. Then the time series of motifs were adjusted in different ways, so they are more convenient to use with machine learning algorithms. We trained a neural network to predict players’ chess moves from their eye movements. The developed method for motif detection seems to work promising, however it has a disadvantage of not being able to perform in positions where very similar motifs exist. strojno učenje sledenje gibanja očesa reševanje problemov modeli človekovega reševanja problema šahovski motivi taktični šahovski problemi šah machine learning eye tracking problem solving models of human problem solving chess motifs tactical chess problems chess true false false Slovenski jezik Angleški jezik Magistrsko delo/naloga 2017-10-13 10:05:02 2017-10-13 10:05:04 2022-08-12 11:41:06 0000-00-00 00:00:00 2017 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 19491 Ursic_Jakob_-_Odkrivanje_sahovskih_motivov_z_analizo_ocesnih_premikov_sahista.pdf Ursic_Jakob_-_Odkrivanje_sahovskih_motivov_z_analizo_ocesnih_premikov_sahista.pdf 1 999426CEE55B5905585BEC41EFE2005A 00eac67118f198f6a2136cb03d1f9517d6ec583d90b1a4f1899cae2bc23cc7e6 f83fcf13-a1b3-11eb-a523-00155dcfd717 20.500.12556/rul/72fac995-bd42-4400-affb-b6b3600ea5e4 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=105495 Fakulteta za računalništvo in informatiko 0 0 0