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Cluster analysis of deep embeddings in real-time strategy games

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. Green open access

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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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