Algoritmi za igranje potezne večakcijske miselne igre Less

V tem delu predstavimo izvedbo in rezultate različnih algoritmov in metod za igranje večakcijske igre Less. Uporabili smo minimaks algoritem, njegovo optimizacijo z alfa-beta rezanjem in drevesno preiskovanje Monte-Carlo. Vse algoritme smo med seboj pomerili v dvobojih in nato analizirali rezultate in vpliv različnih vrednosti vhodnih parametrov algoritmov. Zaradi velikega vejitvenega faktorja igre Less se je drevesno preiskovanje Monte-Carlo izkazalo kot primernejše za igranje igre od minimaks algoritma. V nadaljni analizi smo ugotovili, da na izide iger ne vpliva prednost prve poteze, močno pa vpliva začetna postavitev igralnega polja. Rezultati so pokazali, da najboljši zasnovani algoritmi premagajo priložnostnega igralca igre Less. In this thesis we present implementation and results from different algorithms and methods for playing multi-action game Less. We have used minimax algorithm, its optimization with alpha-beta pruning and Monte-Carlo tree search. All algorithms have played games between themselves and then we have analyzed results and the influence of different input parameters. Due to the huge branching factor of game Less, the Monte-Carlo tree search has proven to be better choice than minimax algorithm. In the following analysis we have discovered, that the first move advantage does not play role in the outcome of the game, while the initial setting of the tiles does. Results have shown that best designed algorithms can beat occasional player of game Less.