Evolving interpretable strategies for zero-sum games

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{MARINO:2022:asoc,

• author = "Julian R. H. Marino and Claudio F. M. Toledo",
• title = "Evolving interpretable strategies for zero-sum games",
• journal = "Applied Soft Computing",
• year = "2022",
• volume = "122",
• pages = "108860",
• keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, RTS Games, Scripts, Intelligent agents, Decision-making",
• ISSN = "1568-4946",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494622002496",
• DOI = "doi:10.1016/j.asoc.2022.108860",
• size = "11 pages",
• abstract = "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 synthesised, scripts require only a tiny fraction of the time needed by search-based methods to decide on the agent next action",
• notes = "Departamento de Sistemas de Computacao, ICMC, Universidade de Sao Paulo, Brazil",

}

Genetic Programming entries for Julian R H Marino Claudio Fabiano Motta Toledo

Citations