Building a player strategy model by analyzing replays of real-time strategy games

Ji Lung Hsieh, Chuen Tsai Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

66 Citations (Scopus)

Abstract

Developing computer-controlled groups to engage in combat, control the use of limited resources, and create units and buildings in Real-Time Strategy(RTS) Games is a novel application in game AI. However, tightly controlled online commercial game pose challenges to researchers interested in observing player activities, constructing player strategy models, and developing practical AI technology in them. Instead of setting up new programming environments or building a large amount of agent's decision rules by player's experience for conducting real-time AI research, the authors use replays of the commercial RTS game StarCraft to evaluate human player behaviors and to construct an intelligent system to learn human-like decisions and behaviors. A case-based reasoning approach was applied for the purpose of training our system to learn and predict player strategies. Our analysis indicates that the proposed system is capable of learning and predicting individual player strategies, and that players provide evidence of their personal characteristics through their building construction order.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages3106-3111
Number of pages6
DOIs
Publication statusPublished - 2008 Nov 24
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 2008 Jun 12008 Jun 8

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period08/6/108/6/8

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Hsieh, J. L., & Sun, C. T. (2008). Building a player strategy model by analyzing replays of real-time strategy games. In 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 3106-3111). [4634237] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2008.4634237