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Discovering Meaningful Labelings for RTS Game Replays via Replay Embeddings
Conference proceeding

Discovering Meaningful Labelings for RTS Game Replays via Replay Embeddings

Pavan Kantharaju, Santiago Ontanon and IEEE
2020 IEEE CONFERENCE ON GAMES (IEEE COG 2020), v 2020-, pp 160-167
01 Jan 2020
DOI: https://doi.org/10.1109/CoG47356.2020.9231556

Abstract

Computer Science Computer Science, Artificial Intelligence Computer Science, Software Engineering Science & Technology Technology
Real-Time Strategy (RTS) games are an interesting environment to study challenging AI problems, such as real-time adversarial planning and opponent modeling. In this paper we focus on approaches that make use of replay data, which usually encode domain expert knowledge of gameplay. Some of these approaches use supervised learning to learn player/agent strategy models and thus rely on these replays being annotated with specific strategies or other labels. However, replays do not usually contain labels for these strategies. The problem we address in this paper is the automatic discovery of meaningful labeling of replays in RTS games. We address this problem by learning action and replay embeddings via recursive neural network models such as LSTMs. These embedded replays can then be clustered to discover labelings by using the clusters as the labels. We show that we can learn embeddings and discover labelings for replays that are correlated with meaningful information from those replays.

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1 citations in Web of Science
1 citations in Scopus

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Collaboration types
Industry collaboration
Domestic collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
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