Conference Proceeding Article
In-game actions of real-time strategy (RTS) games are extremely useful in determining the players' strategies, analyzing their behaviors and recommending ways to improve their play skills. Unfortunately, unstructured sequences of in-game actions are hardly informative enough for these analyses. The inconsistency we observed in human annotation of in-game data makes the analytical task even more challenging. In this paper, we propose an integrated system for in-game action segmentation and semantic label assignment based on a Conditional Random Fields (CRFs) model with essential features extracted from the in-game actions. Our experiments demonstrate that the accuracy of our solution can be as high as 98.9%.
Computer games, Feature extraction, Conditional random fields, Feature extraction, Human annotation, In-game action list segmentation, Real-time strategy games, Semantic label assignment
Databases and Information Systems
Computational Intelligence and Games (CIG), 2012 IEEE Conference on
GONG, Wei; LIM, Ee-Peng; ACHANANUPARP, Palakorn; ZHU, Feida; LO, David; and CHUA, Freddy Chong-Tat.
In-game action list segmentation and labeling in real-time strategy games. (2012). Computational Intelligence and Games (CIG), 2012 IEEE Conference on. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3482
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