Game Tree Search Based on Nondeterministic Action Scripts in Real-Time Strategy Games

Significant progress has been made in recent years toward stronger real-time strategy (RTS) game playing agents. Some of the latest approaches have focused on enhancing standard game tree search techniques with a smart sampling of the search space, or on directly reducing this search space. However, experiments have thus far only been performed using small scenarios. We provide experimental results on the performance of these agents on increasingly larger scenarios. Our main contribution is Puppet Search, a new adversarial search framework that reduces the search space by using scripts that can expose choice points to a look-ahead search procedure. Selecting a combination of a script and decisions for its choice points represents an abstract move to be applied next. Such moves can be directly executed in the actual game, or in an abstract representation of the game state, which can be used by an adversarial tree search algorithm. We tested Puppet Search in μRTS, an abstract RTS game popular within the research community, allowing us to directly compare our algorithm against state-of-the-art agents published in the last few years. We show a similar performance to other scripted and search based agents on smaller scenarios, while outperforming them on larger ones.