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

Copyright Owner and License

LARC

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.

Additional URL

http://doi.org/10.1109/CIG.2012.6374150

Comments

Data set available at http://ink.library.smu.edu.sg/data/1/

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