Haley, J;
Wearne, A;
Copland, C;
Ortiz, E;
Bond, A;
Van Lent, M;
Smith, R;
(2020)
Cluster analysis of deep embeddings in real-time strategy games.
In:
Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II.
(pp. 114131I -114131I).
SPIE: Online Only.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Cluster analysis of deep embeddings in real-time strategy games |
| Event: | SPIE Defense + Commercial Sensing, |
| ISBN-13: | 9781510636033 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2558105 |
| Publisher version: | http://dx.doi.org/10.1117/12.2558105 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10108064 |
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