Building a Player Strategy Model by Analyzing Replays of Real-Time Strategy Games

TitleBuilding a Player Strategy Model by Analyzing Replays of Real-Time Strategy Games
Publication TypeConference Paper
Year of Publication2008
AuthorsHsieh J-L, Sun C-T
Conference Name2008 IEEE International Joint Conference on Neural Networks
Date Published09/2008
Publisher IEEE
Conference LocationHong Kong, China
ISBN Number978-1-4244-1820-6
Accession Number 10365336

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.