{"id":{"repo_id":"catalunya","oai_identifier":"oai:openaccess.uoc.edu:10609/153421"},"canonical_url":"https://search.dev.ndltd.org/etd/catalunya/oai:openaccess.uoc.edu:10609/153421","repository":{"repo_id":"catalunya","name":"Universitat Oberta de Catalunya","base_url":"https://openaccess.uoc.edu/server/oai/request"},"display":{"title":"Metagame analysis in card-based video games: balancing insights through genetic algorithms","abstract":"Video games predicated upon card-based mechanics have experienced a notable surge in popularity in recent years. This phenomenon has precipitated the proliferation of diverse subgenres, among which digital collectible card games and deck-building games are particularly prominent. A recurrent issue within any game which partakes of these core mechanics is the emergence of overly potent strategies or card combinations. Such imbalances may culminate in the dominance of a restricted set of decks within the competitive scene, also known as the metagame. This often engenders dissatisfaction and frustration within the player community, potentially leading to an adverse critical reception or a decline in the active user base. The metagame presents a significant challenge for game designers throughout the development lifecycle of a video game and its subsequent expansions, as they strive to deliver a robustly balanced experience that fosters strategic diversity in deck construction. Furthermore, traditional balancing methodologies, such as manual playtesting, prove insufficient given the extensive combinatorial complexity inherent to these systems, thereby impeding an exhaustive exploration of the strategy space. Consequently, a discernible need exists for computational tools capable of assisting designers in the proactive identification of powerful synergies and potentially game-breaking configurations. This master's degree final project investigates the integration of artificial intelligence techniques, specifically genetic algorithms, as an intrinsic component of the design and balancing workflow for video games featuring deck-building dynamics, and aims to validate the potential of this methodology through a proof of concept.","abstract_html":"Video games predicated upon card-based mechanics have experienced a notable surge in popularity in recent years. This phenomenon has precipitated the proliferation of diverse subgenres, among which digital collectible card games and deck-building games are particularly prominent. A recurrent issue within any game which partakes of these core mechanics is the emergence of overly potent strategies or card combinations. Such imbalances may culminate in the dominance of a restricted set of decks within the competitive scene, also known as the metagame. This often engenders dissatisfaction and frustration within the player community, potentially leading to an adverse critical reception or a decline in the active user base. The metagame presents a significant challenge for game designers throughout the development lifecycle of a video game and its subsequent expansions, as they strive to deliver a robustly balanced experience that fosters strategic diversity in deck construction. Furthermore, traditional balancing methodologies, such as manual playtesting, prove insufficient given the extensive combinatorial complexity inherent to these systems, thereby impeding an exhaustive exploration of the strategy space. Consequently, a discernible need exists for computational tools capable of assisting designers in the proactive identification of powerful synergies and potentially game-breaking configurations. This master's degree final project investigates the integration of artificial intelligence techniques, specifically genetic algorithms, as an intrinsic component of the design and balancing workflow for video games featuring deck-building dynamics, and aims to validate the potential of this methodology through a proof of concept.","abstract_has_math":false,"creators":["Sabater Serna, Beatriz"],"institution":"Universitat Oberta de Catalunya (UOC)","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-06-11","date_published":"2025-06-11","updated_at":"2026-07-27T19:07:20Z","subjects":["Artificial Intelligence","video games","genetic algorithms","Inteligencia Artificial","videojuegos","algoritmos genéticos","Intel·ligència artificial","videojocs","algorismes genètics"],"languages":["eng","spa"],"rights":["CC BY-NC-SA"],"rights_urls":["https://creativecommons.org/licenses/by-nc-sa/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10609/153421","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Nunez do Rio, Joan M"]},{"key":"dc:creator","label":"Author","values":["Sabater Serna, Beatriz"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-15T19:00:45Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-15T19:00:45Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-06-11"]},{"key":"dc:publisher","label":"Institution","values":["Universitat Oberta de Catalunya (UOC)"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/masterThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Intelligence","video games","genetic algorithms","Inteligencia Artificial","videojuegos","algoritmos genéticos","Intel·ligència artificial","videojocs","algorismes genètics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng","spa"]},{"key":"dc:rights","label":"Dc Rights","values":["CC BY-NC-SA"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by-nc-sa/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10609/153421"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Video games predicated upon card-based mechanics have experienced a notable surge in popularity in recent years. This phenomenon has precipitated the proliferation of diverse subgenres, among which digital collectible card games and deck-building games are particularly prominent. A recurrent issue within any game which partakes of these core mechanics is the emergence of overly potent strategies or card combinations. Such imbalances may culminate in the dominance of a restricted set of decks within the competitive scene, also known as the metagame. This often engenders dissatisfaction and frustration within the player community, potentially leading to an adverse critical reception or a decline in the active user base. The metagame presents a significant challenge for game designers throughout the development lifecycle of a video game and its subsequent expansions, as they strive to deliver a robustly balanced experience that fosters strategic diversity in deck construction. Furthermore, traditional balancing methodologies, such as manual playtesting, prove insufficient given the extensive combinatorial complexity inherent to these systems, thereby impeding an exhaustive exploration of the strategy space. Consequently, a discernible need exists for computational tools capable of assisting designers in the proactive identification of powerful synergies and potentially game-breaking configurations. This master's degree final project investigates the integration of artificial intelligence techniques, specifically genetic algorithms, as an intrinsic component of the design and balancing workflow for video games featuring deck-building dynamics, and aims to validate the potential of this methodology through a proof of concept.","Los videojuegos basados en mecánicas de cartas han experimentado un auge de popularidad en los últimos años. Este fenómeno ha dado lugar a la proliferación de diversos subgéneros, entre los cuales destacan los juegos de cartas coleccionables digitales y los juegos de construcción de mazos. Una problemática recurrente en cualquier juego que utiliza estas mecánicas básicas es la aparición de estrategias o combinaciones de cartas excesivamente poderosas. Estos desequilibrios pueden culminar en el dominio de un conjunto extremadamente limitado de mazos en la escena competitiva (metajuego), lo que puede generar insatisfacción y frustración en la comunidad de jugadores, así como derivar en una recepción crítica negativa o incluso en una disminución de la base de usuarios activa. El metajuego presenta un desafío para los diseñadores durante el desarrollo del videojuego y sus expansiones, quienes aspiran ofrecer un equilibrio robusto y un panorama estratégico diverso en la construcción de mazos. Este desafío se acrecienta al constatar que las metodologías de equilibrado tradicionales, como el playtesting manual, resultan insuficientes dada la vasta complejidad combinatoria del sistema, lo que dificulta la exploración exhaustiva del espacio de estrategias. En consecuencia, se evidencia una necesidad de herramientas computacionales que asistan a los diseñadores en la detección proactiva de sinergias y configuraciones potencialmente desequilibradas. El presente trabajo final de máster investiga la incorporación de técnicas de inteligenciaartificial, concretamente algoritmos genéticos, como componente intrínseco del proceso de diseño y equilibrado de videojuegos con dinámicas de construcción de mazos, y aspira a validar el potencial de esta metodología a través de una prueba de concepto."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf","video/mp4"]},{"key":"dc:title","label":"Title","values":["Metagame analysis in card-based video games: balancing insights through genetic algorithms"]}]}],"canonical_facts":{"dc:contributor.other":["Nunez do Rio, Joan M"],"dc:creator":["Sabater Serna, Beatriz"],"dc:date.accessioned":["2025-09-15T19:00:45Z"],"dc:date.available":["2025-09-15T19:00:45Z"],"dc:date.issued":["2025-06-11"],"dc:description.abstract":["Video games predicated upon card-based mechanics have experienced a notable surge in popularity in recent years. This phenomenon has precipitated the proliferation of diverse subgenres, among which digital collectible card games and deck-building games are particularly prominent. A recurrent issue within any game which partakes of these core mechanics is the emergence of overly potent strategies or card combinations. Such imbalances may culminate in the dominance of a restricted set of decks within the competitive scene, also known as the metagame. This often engenders dissatisfaction and frustration within the player community, potentially leading to an adverse critical reception or a decline in the active user base. The metagame presents a significant challenge for game designers throughout the development lifecycle of a video game and its subsequent expansions, as they strive to deliver a robustly balanced experience that fosters strategic diversity in deck construction. Furthermore, traditional balancing methodologies, such as manual playtesting, prove insufficient given the extensive combinatorial complexity inherent to these systems, thereby impeding an exhaustive exploration of the strategy space. Consequently, a discernible need exists for computational tools capable of assisting designers in the proactive identification of powerful synergies and potentially game-breaking configurations. This master's degree final project investigates the integration of artificial intelligence techniques, specifically genetic algorithms, as an intrinsic component of the design and balancing workflow for video games featuring deck-building dynamics, and aims to validate the potential of this methodology through a proof of concept.","Los videojuegos basados en mecánicas de cartas han experimentado un auge de popularidad en los últimos años. Este fenómeno ha dado lugar a la proliferación de diversos subgéneros, entre los cuales destacan los juegos de cartas coleccionables digitales y los juegos de construcción de mazos. Una problemática recurrente en cualquier juego que utiliza estas mecánicas básicas es la aparición de estrategias o combinaciones de cartas excesivamente poderosas. Estos desequilibrios pueden culminar en el dominio de un conjunto extremadamente limitado de mazos en la escena competitiva (metajuego), lo que puede generar insatisfacción y frustración en la comunidad de jugadores, así como derivar en una recepción crítica negativa o incluso en una disminución de la base de usuarios activa. El metajuego presenta un desafío para los diseñadores durante el desarrollo del videojuego y sus expansiones, quienes aspiran ofrecer un equilibrio robusto y un panorama estratégico diverso en la construcción de mazos. Este desafío se acrecienta al constatar que las metodologías de equilibrado tradicionales, como el playtesting manual, resultan insuficientes dada la vasta complejidad combinatoria del sistema, lo que dificulta la exploración exhaustiva del espacio de estrategias. En consecuencia, se evidencia una necesidad de herramientas computacionales que asistan a los diseñadores en la detección proactiva de sinergias y configuraciones potencialmente desequilibradas. El presente trabajo final de máster investiga la incorporación de técnicas de inteligenciaartificial, concretamente algoritmos genéticos, como componente intrínseco del proceso de diseño y equilibrado de videojuegos con dinámicas de construcción de mazos, y aspira a validar el potencial de esta metodología a través de una prueba de concepto."],"dc:format.mimetype":["application/pdf","video/mp4"],"dc:identifier.uri":["https://hdl.handle.net/10609/153421"],"dc:language.iso":["eng","spa"],"dc:publisher":["Universitat Oberta de Catalunya (UOC)"],"dc:rights":["CC BY-NC-SA"],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-sa/4.0/"],"dc:subject":["Artificial Intelligence","video games","genetic algorithms","Inteligencia Artificial","videojuegos","algoritmos genéticos","Intel·ligència artificial","videojocs","algorismes genètics"],"dc:title":["Metagame analysis in card-based video games: balancing insights through genetic algorithms"],"dc:type":["info:eu-repo/semantics/masterThesis"]},"updated_at":"2026-07-27T19:07:20Z"}