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21 April 2020 Cluster analysis of deep embeddings in real-time strategy games
Joshua Haley, Adam Wearne, Cameron Copland, Eric Ortiz, Amanda Bond, Mike van Lent, Rob Smith
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Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114131I (2020) https://doi.org/10.1117/12.2558105
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
Given that many readily available datasets consist of large amounts of unlabeled data,1 unsupervised learning methods are an important component of many data-driven applications. In many instances, ground-state truth labels may be unavailable or obtainable only at a costly expense. As a result, there is an acute need for the ability to understand and interpret unlabeled datasets as thoroughly as possible. In this article, we examine the effectiveness of learned deep embeddings via internal clustering metrics on a dataset comprised of unlabelled StarCraft 2 game replays. The results of this work indicate that the use of deep embeddings provides a promising basis for clustering and interpreting player behavior in complex game domains.
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Joshua Haley, Adam Wearne, Cameron Copland, Eric Ortiz, Amanda Bond, Mike van Lent, and Rob Smith "Cluster analysis of deep embeddings in real-time strategy games", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131I (21 April 2020); https://doi.org/10.1117/12.2558105
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KEYWORDS
Machine learning
Visualization
New and emerging technologies
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