Evolving interpretable strategies for zero-sum games
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- @Article{MARINO:2022:asoc,
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author = "Julian R. H. Marino and Claudio F. M. Toledo",
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title = "Evolving interpretable strategies for zero-sum games",
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journal = "Applied Soft Computing",
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year = "2022",
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volume = "122",
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pages = "108860",
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keywords = "genetic algorithms, genetic programming, Evolutionary
algorithm, RTS Games, Scripts, Intelligent agents,
Decision-making",
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ISSN = "1568-4946",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494622002496",
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DOI = "doi:10.1016/j.asoc.2022.108860",
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size = "11 pages",
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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",
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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