TY - GEN
T1 - Predicting resource locations in game maps using deep convolutional neural networks
AU - Lee, Scott
AU - Isaksen, Aaron
AU - Holmgard, Christoffer
AU - Togelius, Julian
N1 - Publisher Copyright:
©2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - We describe an application of neural networks to predict the placements of resources in StarCraft II maps. Networks are trained on existing maps taken from databases of maps actively used in online competitions and tested on unseen maps with resources (minerals and vespene gas) removed. This method is potentially useful for Al-Assisted game design tools, allowing the suggestion of resource and base placements consonant with implicit StarCraft II design principles for fully or partially sketched heightmaps. By varying the thresholds for the placement of resources, more or fewer resources can be created consistently with the pattern of a single map. We further propose that these networks can be used to help understand the design principles of StarCraft II maps, and by extension other, similar types of game content.
AB - We describe an application of neural networks to predict the placements of resources in StarCraft II maps. Networks are trained on existing maps taken from databases of maps actively used in online competitions and tested on unseen maps with resources (minerals and vespene gas) removed. This method is potentially useful for Al-Assisted game design tools, allowing the suggestion of resource and base placements consonant with implicit StarCraft II design principles for fully or partially sketched heightmaps. By varying the thresholds for the placement of resources, more or fewer resources can be created consistently with the pattern of a single map. We further propose that these networks can be used to help understand the design principles of StarCraft II maps, and by extension other, similar types of game content.
UR - http://www.scopus.com/inward/record.url?scp=85021974049&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85021974049
T3 - AAAI Workshop - Technical Report
SP - 46
EP - 52
BT - WS-16-21
PB - AI Access Foundation
T2 - 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016
Y2 - 8 October 2016 through 9 October 2016
ER -