In-game action list segmentation and labeling in real-time strategy games

In-game action list segmentation and labeling in real-time strategy games

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....

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Bibliographic Details
Main Authors: GONG, Wei, LIM, Ee-Peng, ACHANANUPARP, Palakorn, ZHU, Feida, LO, David, CHUA, Freddy Chong-Tat
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
Subjects:
Computer games
Feature extraction
Conditional random fields
Human annotation
In-game action list segmentation
Real-time strategy games
Semantic label assignment
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/3482
https://ink.library.smu.edu.sg/context/sis_research/article/4483/viewcontent/C29___In_Game_Action_List_Segmentation_and_Labeling_in_Real_Time_Strategy_Games__CIG2012_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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%.

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