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Evolving interpretable strategies for zero-sum games
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DOI：10.1016/j.asoc.2022.108860.png)
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摘要
En 中文
The present paper introduces Gesy, a genetic programming approach to script synthesis for zero-sum games. We will explore the sum-zero game context in Real-Time Strategy (RTS) games, where players must look for strategies (planning of actions) to maximize their gains or minimize their losses. The goal is to solve the script synthesis problem, which demands the synthesis of a computer program from a space of programs defined by a Domain-Specific Language (DSL). The synthesized program must encode a practical strategy for zero-sum games. Empirical results validate Gesy using the mu RTS platform, an academic test bed game that presents the main features found in RTS commercial games. The results show that our method provides interpretable strategies that are competitive with state-of-the-art search-based approaches in terms of play strength. Moreover, once synthesized, scripts require only a tiny fraction of the time needed by search-based methods to decide on the agent's next action. (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Evolutionary algorithm
RTS Games
Scripts
Intelligent agents
Decision-making
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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