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• W3177194733 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." @default.
• W3177194733 created "2021-07-05" @default.
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• W3177194733 date "2021-05-18" @default.
• W3177194733 modified "2023-09-24" @default.
• W3177194733 title "Programmatic Strategies for Real-Time Strategy Games" @default.
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• W3177194733 doi "https://doi.org/10.1609/aaai.v35i1.16114" @default.
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