A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing

In video games, the validation of design specifications throughout the development process poses a major challenge as the project grows in complexity and scale and purely manual testing becomes very costly. This article proposes a new approach to design validation regression testing based on a reinf...

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Detalles Bibliográficos
Autores: Gutiérrez Sánchez, Pablo, Gómez Martín, Marco Antonio, González Calero, Pedro Antonio, Gómez Martín, Pedro Pablo
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/131033
Acceso en línea:https://hdl.handle.net/20.500.14352/131033
Access Level:acceso abierto
Palabra clave:Testing
Games
Task analysis
Chatbots
Video games
Reinforcement learning
Logic
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
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oai_identifier_str oai:docta.ucm.es:20.500.14352/131033
network_acronym_str ES
network_name_str España
repository_id_str
spelling A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression TestingGutiérrez Sánchez, PabloGómez Martín, Marco AntonioGonzález Calero, Pedro AntonioGómez Martín, Pedro PabloTestingGamesTask analysisChatbotsVideo gamesReinforcement learningLogicInteligencia artificial (Informática)1203.04 Inteligencia ArtificialIn video games, the validation of design specifications throughout the development process poses a major challenge as the project grows in complexity and scale and purely manual testing becomes very costly. This article proposes a new approach to design validation regression testing based on a reinforcement learning technique guided by tasks expressed in a formal logic specification language (truncated linear temporal logic) and the progress made in completing these tasks. This requires no prior knowledge of machine learning to train testing bots, is naturally interpretable and debuggable, and produces dense reward functions without the need for reward shaping. We investigate the validity of our strategy by comparing it to an imitation baseline in experiments organized around three use cases of typical scenarios in commercial video games on a 3-D stealth testing environment created in unity. For each scenario, we analyze the agents' reactivity to modifications in common assets to accommodate design needs in other sections of the game, and their ability to report unexpected gameplay variations. Our experiments demonstrate the practicality of our approach for training bots to conduct automated regression testing in complex video game settings.IEEEUniversidad Complutense de Madrid20242024-12-0120242024-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/131033reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1310332026-06-02T12:44:21Z
dc.title.none.fl_str_mv A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
title A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
spellingShingle A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
Gutiérrez Sánchez, Pablo
Testing
Games
Task analysis
Chatbots
Video games
Reinforcement learning
Logic
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
title_short A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
title_full A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
title_fullStr A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
title_full_unstemmed A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
title_sort A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing
dc.creator.none.fl_str_mv Gutiérrez Sánchez, Pablo
Gómez Martín, Marco Antonio
González Calero, Pedro Antonio
Gómez Martín, Pedro Pablo
author Gutiérrez Sánchez, Pablo
author_facet Gutiérrez Sánchez, Pablo
Gómez Martín, Marco Antonio
González Calero, Pedro Antonio
Gómez Martín, Pedro Pablo
author_role author
author2 Gómez Martín, Marco Antonio
González Calero, Pedro Antonio
Gómez Martín, Pedro Pablo
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Testing
Games
Task analysis
Chatbots
Video games
Reinforcement learning
Logic
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
topic Testing
Games
Task analysis
Chatbots
Video games
Reinforcement learning
Logic
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
description In video games, the validation of design specifications throughout the development process poses a major challenge as the project grows in complexity and scale and purely manual testing becomes very costly. This article proposes a new approach to design validation regression testing based on a reinforcement learning technique guided by tasks expressed in a formal logic specification language (truncated linear temporal logic) and the progress made in completing these tasks. This requires no prior knowledge of machine learning to train testing bots, is naturally interpretable and debuggable, and produces dense reward functions without the need for reward shaping. We investigate the validity of our strategy by comparing it to an imitation baseline in experiments organized around three use cases of typical scenarios in commercial video games on a 3-D stealth testing environment created in unity. For each scenario, we analyze the agents' reactivity to modifications in common assets to accommodate design needs in other sections of the game, and their ability to report unexpected gameplay variations. Our experiments demonstrate the practicality of our approach for training bots to conduct automated regression testing in complex video game settings.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-01
2024
2024-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
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 https://hdl.handle.net/20.500.14352/131033
url https://hdl.handle.net/20.500.14352/131033
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
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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