Applying LCS/XCS to the RTS Games Domain

Real-Time Strategy games (RTS) are representatives of the highest class of computational complexity in computer game genres. To cope with the high complexity of the state-action space of RTS game worlds, various Machine Learning algorithms are being used and researched extensively. In this article,...

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Autores principales:	Damijan Novak*, Domen Verber
Formato:	article
Lenguaje:	EN
Publicado:	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2021
Materias:	AI game agent LCS micro RTS real-time strategy games XCS Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/fc773c34b3884662a0a62469cd512946
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id	oai:doaj.org-article:fc773c34b3884662a0a62469cd512946
record_format	dspace
spelling	oai:doaj.org-article:fc773c34b3884662a0a62469cd5129462021-11-07T00:38:44ZApplying LCS/XCS to the RTS Games Domain1330-36511848-6339https://doaj.org/article/fc773c34b3884662a0a62469cd5129462021-01-01T00:00:00Zhttps://hrcak.srce.hr/file/385035https://doaj.org/toc/1330-3651https://doaj.org/toc/1848-6339Real-Time Strategy games (RTS) are representatives of the highest class of computational complexity in computer game genres. To cope with the high complexity of the state-action space of RTS game worlds, various Machine Learning algorithms are being used and researched extensively. In this article, we apply eXtended Classifier Systems (XCS) to the domain of RTS games. The XCS algorithm belongs to a Learning Classifier Systems (LCS) group known for their adaptability, generalisation, and scalability. We build the game agent named AIXCS. It uses a group of XCS algorithms, which generate a set of unit-actions used in the RTS game. The AIXCS operates without prior learning from the game runs and in tight timing constraints. The AIXCS was put to the test against other game agents in the micro RTS game environment, with positive results regarding successful game operation at runtime.Damijan Novak*Domen VerberFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek articleAIgame agentLCSmicro RTSreal-time strategy gamesXCSEngineering (General). Civil engineering (General)TA1-2040ENTehnički Vjesnik, Vol 28, Iss 6, Pp 2127-2137 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AI game agent LCS micro RTS real-time strategy games XCS Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	AI game agent LCS micro RTS real-time strategy games XCS Engineering (General). Civil engineering (General) TA1-2040 Damijan Novak* Domen Verber Applying LCS/XCS to the RTS Games Domain
description	Real-Time Strategy games (RTS) are representatives of the highest class of computational complexity in computer game genres. To cope with the high complexity of the state-action space of RTS game worlds, various Machine Learning algorithms are being used and researched extensively. In this article, we apply eXtended Classifier Systems (XCS) to the domain of RTS games. The XCS algorithm belongs to a Learning Classifier Systems (LCS) group known for their adaptability, generalisation, and scalability. We build the game agent named AIXCS. It uses a group of XCS algorithms, which generate a set of unit-actions used in the RTS game. The AIXCS operates without prior learning from the game runs and in tight timing constraints. The AIXCS was put to the test against other game agents in the micro RTS game environment, with positive results regarding successful game operation at runtime.
format	article
author	Damijan Novak* Domen Verber
author_facet	Damijan Novak* Domen Verber
author_sort	Damijan Novak*
title	Applying LCS/XCS to the RTS Games Domain
title_short	Applying LCS/XCS to the RTS Games Domain
title_full	Applying LCS/XCS to the RTS Games Domain
title_fullStr	Applying LCS/XCS to the RTS Games Domain
title_full_unstemmed	Applying LCS/XCS to the RTS Games Domain
title_sort	applying lcs/xcs to the rts games domain
publisher	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
publishDate	2021
url	https://doaj.org/article/fc773c34b3884662a0a62469cd512946
work_keys_str_mv	AT damijannovak applyinglcsxcstothertsgamesdomain AT domenverber applyinglcsxcstothertsgamesdomain
_version_	1718443649211039744

Applying LCS/XCS to the RTS Games Domain

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