Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Content evaluation of StarCraft maps using Neuroevolution
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context. Games are becoming larger and the amount of assets required is increasing. Game studios turn toward procedural generation to ease the load of asset creation. After the game is released the studios want to extend the longevity of their creation. One way of doing this is to open up the game for community created add-ons and assets or utilize some procedural content generation. Both community created assets and procedural generation comes with a classification problem to filter out the undesirable content.

Objectives. This thesis will attempt to create a method to evaluate community-generated StarCraft maps with the help of machine learning.

Methods. Manually extracted metrics from StarCraft maps and ratings from community repositories. This data is used to train neural networks using NeuroEvolution of Augmenting Topologies (NEAT). The method will be compared with Sequential Minimal Optimization (SMO) and ZeroR.

Results and Conclusions. The problem turned out to be more difficult than initially thought. The results using NEAT are marginally better than SMO and ZeroR. The suspected reason for this is insufficient input data and/or bad input parameters. Further experimentation could be conducted with deep learning to try to find a suitable solution for this problem.

Place, publisher, year, edition, pages
2016. , 40 p.
Keyword [en]
neural networks, NEAT, automated evaluation, StarCraft
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-11684OAI: oai:DiVA.org:bth-11684DiVA: diva2:908962
Subject / course
DV2524 Degree Project in Computer Science for Engineers
Educational program
PAACI Master of Science in Game and Software Engineering
Supervisors
Examiners
Available from: 2016-03-04 Created: 2016-03-03 Last updated: 2016-03-14Bibliographically approved

Open Access in DiVA

fulltext(1627 kB)178 downloads
File information
File name FULLTEXT03.pdfFile size 1627 kBChecksum SHA-512
1076aea2d3551eb278aec5df7cb4b45ff76f5aadeb073af404118d9a816f694c7a29300489786e5ea6d7dd29f603daffbd2015284075d3cafc3d010b314d7fbb
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Larsson, SebastianPetri, Ossian
By organisation
Department of Creative Technologies
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 178 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 264 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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