Combining Expert Knowledge and Learning from Demonstration in Real-Time Strategy Games

TitleCombining Expert Knowledge and Learning from Demonstration in Real-Time Strategy Games
Publication TypeConference Paper
Year of Publication2011
AuthorsPalma R, Sánchez-Ruiz AA, Gómez-Martín MAntonio, Gómez-Martín PPablo, González-Calero PAntonio
Conference Name19th International Conference on Case-Based Reasoning
PublisherSpringer
Conference LocationLondon, UK
Abstract

Case-based planning (CBP) is usually considered a good solution
to solve the knowledge acquisition problem that arises when developing
AIs for real-time strategy games. Unlike more classical approaches,
such as state machines or rule-based systems, CBP allows experts to train
AIs directly from games recorded by expert players. Unfortunately, this
simple approach has also some drawbacks, for example it is not easy to
refine an existing case base to learn specific strategies when a long game
session is needed to create a new trace. Furthermore, CBP may be too
reactive to small changes in the game state and, at the same time, do not
respond fast enough to important changes in the opponent’s strategy. We
propose to alleviate these problems by letting experts to inject decision
making knowledge into the system in the form of behavior trees, and we
show promising results in some experiments using Starcraft