Metagame analysis in card-based video games: balancing insights through genetic algorithms

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.

Grantor dc:publisher

Universitat Oberta de Catalunya (UOC)

Year dc:date.issued

2025

Author dc:creator

• Sabater Serna, Beatriz

• Artificial Intelligence
• video games
• genetic algorithms
• Inteligencia Artificial
• videojuegos
• algoritmos genéticos
• Intel·ligència artificial
• videojocs
• algorismes genètics

Statement dc:rights

• CC BY-NC-SA

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-sa/4.0/

Language dc:language.iso

eng, spa

Handle dc:identifier.uri

https://hdl.handle.net/10609/153421

OAI identifier oai:identifier

oai:openaccess.uoc.edu:10609/153421