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Crushing Candy Crush: Predicting Human Success Rate in a Mobile Game using Monte-Carlo Tree Search
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The purpose of this thesis is to evaluate the possibility of predicting difficulty, measured in average human success rate (AHSR), across game levels of a mobile game using a general AI algorithm. We implemented and tested a simulation based bot using MCTS for Candy. Our results indicate that AHSR can be predicted accurately using MCTS, which in turn suggests that our bot could be used to streamline game level development. Our work is relevant to the field of AI, especially the subfields of MCTS and single-player stochastic games as Candy, with its diverse set of features, proved an excellent new challenge for testing the general capabilities of MCTS. The results will also be valuable to companies interested in using AI for automatic testing of software. 

Place, publisher, year, edition, pages
2017.
Keyword [en]
MCTS Tree Search Simulation
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-206595OAI: oai:DiVA.org:kth-206595DiVA: diva2:1093469
External cooperation
King
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2017-05-08 Created: 2017-05-07 Last updated: 2017-05-08Bibliographically approved

Open Access in DiVA

fulltext(11163 kB)65 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf