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Drevesno preiskovanje Monte Carlo s Thompsonovim vzorčenjem pri igri Prebivalci otoka Catan
Tuma, Katja (Author), Šter, Branko (Mentor) More about this mentor... This link opens in a new window, Nilsson, Bengt J. (Co-mentor)

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Abstract
Drevesno preiskovanje Monte Carlo (MCTS) je ena izmed najbolj uporabljenih metod pri implementaciji močnega računalniškega igralca iger v umetni inteligenci, brez uporabe predhodnega znanja o domeni. Najmočnejši in najbolj popularni algoritmi, ki se pogosto uporabljajo za rešitev t.i. dileme raziskovanja (engl. exploration) proti izkoriščanju znanja (engl. exploitation) pri problemu več-rokih banditov, so raziskani in predstavljeni s pomočjo pregleda literature. Na podlagi empiričnih študij Thompsonovega vzorčenja v primerjavi s pristopom zgornje meje zaupanja (UCB) ter različicami podobnih algoritmov smo v magistrskem delu spremenili drevesno strategijo širjenja v MCTS. Končna domena aplikacije spremenjenega algoritma je družabna igra Prebivalci otoka Catan (SoC), implementirana v programskem jeziku C, skupaj z MCTS-UCT agentom, MCTS-TS agentom ter dvema preprosto igrajočima agentoma. Meritve učinkovitosti naštetih agentov prikazujejo povečano moč igranja agenta s spremenjeno drevesno strategijo, v primerjavi z najbolj pogosto uporabljenim pristopom, t.j. UCT.

Language:Slovenian
Keywords:drevesno preiskovanje Monte Carlo (MCTS), več-roki ban- dit (MAB), zgornja meja zaupanja pri drevesih (UCT), Thompsonovo vzorčenje (TS), umetna inteligenca (AI), Prebivalci otoka Catan (SoC).
Work type:Master's thesis/paper (mb22)
Organization:FRI - Faculty of computer and information science
Year:2016
Views:680
Downloads:275
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Secondary language

Language:English
Title:Monte Carlo Tree Search with Thompson sampling in The Settlers of Catan
Abstract:
Monte Carlo Tree search (MCTS) is a popular method of choice for addressing the problem of a strong computer based game playing agent in Artificial Intelligence, without any prior domain knowledge. The strongest and most popular algorithms used to tackle the so-called exploration vs. exploitation dilemma in Multi-armed Bandit (MAB) problems were identified and presented in a literature review. Empirical studies measuring the performance of Thompson sampling (TS) and the state-of-the-art Upper Confidence Bound (UCB) approach in the classical MAB problem have been found, results of which support our modified tree policy in MCTS. The domain of application is the board game of the Settlers of Catan (SoC), which is implemented as a multi-agent environment in the programming language C, along with a MCTS-UCT agent, MCTS-TS agent and two strategy playing agents, namely the ore-grain and wood-clay agent. Performance measurements of the aforementioned agents, presented and discussed in this work, demonstrate an increase in the performance of the agent with the modified tree policy, when compared to the state-of-the-art approach (UCT).

Keywords:Monte Carlo Tree Search (MCTS), Multi-armed Bandits (MAB), Upper Confidence Bound for Trees (UCT), Thompson sam- pling (TS), Artificial Intelligence (AI), the Settlers of Catan (SoC).

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