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Player Analysis in Computer Games Using Artificial Neural Networks
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Star Vault AB is a video game development company that has developed the video game Mortal Online. The company has stated that they believe that players new to the game repeatedly find themselves being lost in the game. The objective of this study is to evaluate whether or not an Artificial Neural Network can be used to evaluate when a player is lost in the game Mortal Online. This is done using the free open source library Fast Artifical Neural Network Library. People are invited to a data collection event where they play a tweaked version of the game to facilitate data collection. Players specify whether they are lost or not and the data collected is flagged accordingly. The collected data is then prepared with different parameters to be used when training multiple Artificial Neural Networks. When creating an Artificial Neural Network there exists several parameters which have an impact on its performance. Performance is defined as the balance of high prediction accuracy against low false positive rate. These parameters vary depending on the purpose of the Artificial Neural Network. A quantitative approach is followed where these parameters are varied to investigate which values result in the Artificial Neural Network which best identifies when a player is lost. The parameters are grouped into stages where all combinations of parameter values within each stage are evaluated to reduce the amount of Artificial Neural Networks which have to be trained, with the best performing parameters of each stage being used in subsequent stages. The result is a set of values for the parameters that are considered as ideal as possible. These parameter values are then altered one at a time to verify that they are ideal. The results show that a set of parameters exist which can optimize the Artificial Neural Network model to identify when a player is lost, however not with the high performance that was hoped for. It is theorized that the ambiguity of the word "lost" and the complexity of the game are critical to the low performance.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Supervised learning, Artificial neural networks, Machine learning, Video games
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-14812OAI: oai:DiVA.org:bth-14812DiVA: diva2:1118395
External cooperation
Star Vault AB
Subject / course
Degree Project in Master of Science in Engineering 30.0
Educational program
PAACI Master of Science in Game and Software Engineering
Supervisors
Examiners
Available from: 2017-06-30 Created: 2017-06-30 Last updated: 2017-06-30Bibliographically approved

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fulltext(7435 kB)13 downloads
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Bergsten, JohnÖhman, Konrad
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • de-DE
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  • Other locale
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Output format
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