Publication Type

Conference Proceeding Article

Publication Date

9-2012

Abstract

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%.

Keywords

Computer games, Feature extraction, Conditional random fields, Feature extraction, Human annotation, In-game action list segmentation, Real-time strategy games, Semantic label assignment

Discipline

Databases and Information Systems

Publication

Computational Intelligence and Games (CIG), 2012 IEEE Conference on

ISBN

978-1-4673-1194-6

Identifier

10.1109/CIG.2012.6374150

Publisher

IEEE

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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