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Predicting Game Level Difficulty Using Deep Neural Networks
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Uppskattning av spelbanors svårighetsgrad med djupa neurala nätverk (Swedish)
Abstract [en]

We explored the usage of Monte Carlo tree search (MCTS) and deep learning in order to predict game level difficulty in Candy Crush Saga (Candy) measured as number of attempts per success. A deep neural network (DNN) was trained to predict moves from game states from large amounts of game play data. The DNN played a diverse set of levels in Candy and a regression model was fitted to predict human difficulty from bot difficulty. We compared our results to an MCTS bot. Our results show that the DNN can make estimations of game level difficulty comparable to MCTS in substantially shorter time. 

Abstract [sv]

Vi utforskade användning av Monte Carlo tree search (MCTS) och deep learning för attuppskatta banors svårighetsgrad i Candy Crush Saga (Candy). Ett deep neural network(DNN) tränades för att förutse speldrag från spelbanor från stora mängder speldata. DNN:en spelade en varierad mängd banor i Candy och en modell byggdes för att förutsemänsklig svårighetsgrad från DNN:ens svårighetsgrad. Resultatet jämfördes medMCTS. Våra resultat indikerar att DNN:ens kan göra uppskattningar jämförbara medMCTS men på substantiellt kortare tid.

Place, publisher, year, edition, pages
2017. , 33 p.
Series
EES Examensarbete / Master Thesis
Keyword [en]
Deep learning, neural networks, machine learning
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-217140OAI: oai:DiVA.org:kth-217140DiVA: diva2:1154062
External cooperation
King
Educational program
Master of Science in Engineering - Computer Science and Technology
Presentation
2017-09-11, Room 4523, Lindstedtsvägen 5, Stockholm, 15:15 (English)
Supervisors
Examiners
Available from: 2017-11-06 Created: 2017-11-01 Last updated: 2017-11-06Bibliographically approved

Open Access in DiVA

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Citation style
  • apa
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