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Action abstractions restrict the number of legal actions available duringsearch in multi-unit real-time adversarial games, thus allowing algorithms tofocus their search on a set of promising actions. Optimal strategies derivedfrom un-abstracted spaces are guaranteed to be no worse than optimal strategiesderived from action-abstracted spaces. In practice, however, due to real-timeconstraints and the state space size, one is only able to derive goodstrategies in un-abstracted spaces in small-scale games. In this paper weintroduce search algorithms that use an action abstraction scheme we callasymmetric abstraction. Asymmetric abstractions retain the un-abstractedspaces' theoretical advantage over regularly abstracted spaces while stillallowing the search algorithms to derive effective strategies, even inlarge-scale games. Empirical results on combat scenarios that arise in areal-time strategy game show that our search algorithms are able tosubstantially outperform state-of-the-art approaches.

Asymmetric Action Abstractions for Multi-Unit Control in Adversarial Real-Time Games