Programmatic Strategies for Real-Time Strategy Games

Authors

  • Julian R. H. Mariño Universidade de São Paulo
  • Rubens O. Moraes Universidade Federal de Viçosa
  • Tassiana C. Oliveira Universidade Federal de Viçosa
  • Claudio Toledo Universidade de São Paulo
  • Levi H. S. Lelis University of Alberta

Keywords:

Software Engineering, Planning/Scheduling and Learning, Games

Abstract

Search-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.

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Published

2021-05-18

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

Issue

Section

AAAI Technical Track on Application Domains