Programmatic Strategies for Real-Time Strategy Games
Keywords:Software Engineering, Planning/Scheduling and Learning, Games
AbstractSearch-based systems have shown to be effective for planning in zero-sum games. However, search-based approaches have important disadvantages. First, the decisions of search algorithms are mostly non-interpretable, which is problematic in domains where predictability and trust are desired such as commercial games. Second, the computational complexity of search-based algorithms might limit their applicability, especially in contexts where resources are shared among other tasks such as graphic rendering. In this work we introduce a system for synthesizing programmatic strategies for a real-time strategy (RTS) game. In contrast with search algorithms, programmatic strategies are more amenable to explanations and tend to be efficient, once the program is synthesized. Our system uses a novel algorithm for simplifying domain-specific languages (DSLs) and a local search algorithm that synthesizes programs with self play. We performed a user study where we enlisted four professional programmers to develop programmatic strategies for mRTS, a minimalist RTS game. Our results show that the programs synthesized by our approach can outperform search algorithms and be competitive with programs written by the programmers.
How to Cite
Mariño, J. R. H., Moraes, R. O., Oliveira, T. C., Toledo, C., & Lelis, L. H. S. (2021). Programmatic Strategies for Real-Time Strategy Games. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 381-389. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16114
AAAI Technical Track on Application Domains