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
Research Areas
Data Management and Analytics
Publication
2012 IEEE Conference on Computational Intelligence and Games CIG : 11-14 September 2012, Granada, Spain: Proceedings
First Page
147
Last Page
154
ISBN
9781467311946
Identifier
10.1109/CIG.2012.6374150
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
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). 2012 IEEE Conference on Computational Intelligence and Games CIG : 11-14 September 2012, Granada, Spain: Proceedings. 147-154. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3482
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Additional URL
http://doi.org/10.1109/CIG.2012.6374150
Comments
Data set available at http://ink.library.smu.edu.sg/data/1/