Overview

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 work, 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.

Members

Wei GONG, Feida ZHU

Dataset

Starcraft II In-Game Action Lists

Publication

"InGame Action List Segmentation and Labeling in Real-Time Strategy Games"
Wei Gong, Ee-Peng Lim, Feida Zhu, Palakorn Achananuparp, David Lo, Freddy Chong Tat Chua Proc. of the 8th IEEE Conference on Computational Intelligence and Games (CIG' 12), Granada, Spain, September, 2012.