An empirical study on collective intelligence algorithms for video games problem-solving

Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken...

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Detalles Bibliográficos
Autores: González-Pardo, Antonio, Palero, Fernando, Camacho, David
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/674486
Acceso en línea:http://hdl.handle.net/10486/674486
Access Level:acceso abierto
Palabra clave:Collective intelligence
Ant colony optimization
Genetic algorithms
Video games solving algorithms
Lemmings video game
Informática
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spelling An empirical study on collective intelligence algorithms for video games problem-solvingGonzález-Pardo, AntonioPalero, FernandoCamacho, DavidCollective intelligenceAnt colony optimizationGenetic algorithmsVideo games solving algorithmsLemmings video gameInformáticaComputational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken an important attention from the research community due to their applicability to real problems. Any CSP problem is usually modelled as a constrained graph where the edges represent a set of restrictions that must be verified by the variables (represented as nodes in the graph) which will define the solution of the problem. This paper studies the performance of two particular CI algorithms, ant colony optimization (ACO) and genetic algorithms (GA), when dealing with graph-constrained models in video games problems. As an application domain, the "Lemmings" video game has been selected, where a set of lemmings must reach the exit point of each level. In order to do that, each level is represented as a graph where the edges store the allowed movements inside the world. The goal of the algorithms is to assign the best skills in each position on a particular level, to guide the lemmings to reach the exit. The paper describes how the ACO and GA algorithms have been modelled and applied to the selected video game. Finally, a complete experimental comparison between both algorithms, based on the number of solutions found and the levels solved, is analysed to study the behaviour of those algorithms in the proposed domain.This work is supported by the Spanish Ministry of Science and Education under Project Code TIN2014-56494-C4-4-P, Comunidad Autonoma de Madrid under project CIBERDINE S2013/ICE-3095, and Savier an Airbus Defense & Space project (FUAM-076914 and FUAM-076915).Slovak Academy of SciencesDepartamento de Ingeniería InformáticaEscuela Politécnica SuperiorAnálisis de Datos e Inteligencia Aplicada (ING EPS-012)20152015-01-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/674486reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6744862026-06-23T12:46:27Z
dc.title.none.fl_str_mv An empirical study on collective intelligence algorithms for video games problem-solving
title An empirical study on collective intelligence algorithms for video games problem-solving
spellingShingle An empirical study on collective intelligence algorithms for video games problem-solving
González-Pardo, Antonio
Collective intelligence
Ant colony optimization
Genetic algorithms
Video games solving algorithms
Lemmings video game
Informática
title_short An empirical study on collective intelligence algorithms for video games problem-solving
title_full An empirical study on collective intelligence algorithms for video games problem-solving
title_fullStr An empirical study on collective intelligence algorithms for video games problem-solving
title_full_unstemmed An empirical study on collective intelligence algorithms for video games problem-solving
title_sort An empirical study on collective intelligence algorithms for video games problem-solving
dc.creator.none.fl_str_mv González-Pardo, Antonio
Palero, Fernando
Camacho, David
author González-Pardo, Antonio
author_facet González-Pardo, Antonio
Palero, Fernando
Camacho, David
author_role author
author2 Palero, Fernando
Camacho, David
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
Análisis de Datos e Inteligencia Aplicada (ING EPS-012)
dc.subject.none.fl_str_mv Collective intelligence
Ant colony optimization
Genetic algorithms
Video games solving algorithms
Lemmings video game
Informática
topic Collective intelligence
Ant colony optimization
Genetic algorithms
Video games solving algorithms
Lemmings video game
Informática
description Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken an important attention from the research community due to their applicability to real problems. Any CSP problem is usually modelled as a constrained graph where the edges represent a set of restrictions that must be verified by the variables (represented as nodes in the graph) which will define the solution of the problem. This paper studies the performance of two particular CI algorithms, ant colony optimization (ACO) and genetic algorithms (GA), when dealing with graph-constrained models in video games problems. As an application domain, the "Lemmings" video game has been selected, where a set of lemmings must reach the exit point of each level. In order to do that, each level is represented as a graph where the edges store the allowed movements inside the world. The goal of the algorithms is to assign the best skills in each position on a particular level, to guide the lemmings to reach the exit. The paper describes how the ACO and GA algorithms have been modelled and applied to the selected video game. Finally, a complete experimental comparison between both algorithms, based on the number of solutions found and the levels solved, is analysed to study the behaviour of those algorithms in the proposed domain.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-01-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/674486
url http://hdl.handle.net/10486/674486
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Slovak Academy of Sciences
publisher.none.fl_str_mv Slovak Academy of Sciences
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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