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Igranje igre "Kamen, papir, škarje, kuščar, Spock" z metodami umetne inteligence
BIZJAK, ALEN (Author), Bosnić, Zoran (Mentor) More about this mentor... This link opens in a new window

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Abstract
Igranje igre "Kamen, papir, škarje, kuščar, Spock" ni zahtevno opravilo, saj je igra preprosta in na prvi pogled podvržena naključju. Bolj zahtevno pa je zmagati oziroma zmagovati na dolgi rok. Z metodami umetne inteligence smo v diplomskem delu na podlagi zgodovine odigranih potez v dani igri poskušali odkriti vzorce v nasprotnikovi strategiji in si zagotoviti pozitivno razmerje zmag in porazov. Pri tem smo se osredotočili izrecno na zgodovino odigranih potez. Ostalih možnih vhodov, kot na primer opazovanje gibanja nasprotnikove roke pri formaciji poteze, nismo uporabili. Za ta namen smo razvili naslednje algoritme: metoda ujemanja preteklih nizov, markovske verige, spodbujevano učenje na podlagi potez in meta-klasifikator na podlagi spodbujevanega učenja. Razvite algoritme smo testirali preko različnih testnih scenarijev, ki so vključevali strojno učenje s pomočjo preprostih pomožnih algoritmov in vnaprej generirane nize potez. Po izvedbi vseh eksperimentov smo algoritme med seboj primerjali in analizirali njihovo uspešnost. Rezultati so pokazali, da sta se v večini primerov najbolje izkazala algoritma markovske verige in meta-klasifikator na podlagi spodbujevanega učenja.

Language:Slovenian
Keywords:umetna inteligenca
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2020
Views:265
Downloads:109
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Secondary language

Language:English
Title:Playing game “Rock, paper, scissors, lizard, Spock” using methods of artificial intelligence
Abstract:
Playing the game "Rock, Paper, Scissors, Lizard, Spock" is not difficult since the game is both simple and, at first glance, subject to chance. It is more challenging to develop a winning strategy that will guarantee a win or many wins in the long run. In this thesis, we used artificial intelligence to try to discover a pattern in the moves of the opponent, focusing specifically on the history of moves in a game to secure a positive win/loss ratio. We did not include other factors, such as observing the movement of the opponent’s hand as they choose which move to use. For this purpose we developed the following algorithms: history string matching, markov chains, reinforcement learning based on moves and meta-classificator with reinforcement learning. We tested our developed algorithms through various test scenarios, which included machine learning with the help of simple auxiliary algorithms and move sequences that were generated in advance. After conducting all experiments, we compared the algorithms and analyzed their performance. The results have shown that in most test scenarios the best performing algorithms were markov chains and meta-classificator with reinforcement learning.

Keywords:artificial intelligence

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