Creating and evaluating games manually is an arduous and laborious task. Procedural content generation can aid by creating game artifacts, but usually not an entire game. Evolutionary game design, which combines evolutionary algorithms with automated playtesting, has been used to create novel board games with simple equipment; however, the original approach does not include complex tabletop games with dice, cards, and maps. This work proposes an extension of the approach for tabletop games, evaluating the process by generating variants of Risk, a military strategy game where players must conquer map territories to win. We achieved this using a genetic algorithm to evolve the chosen parameters, as well as a rules-based agent to test the games and a variety of quality criteria to evaluate the new variations generated. Our results show the creation of new variations of the original game with smaller maps, resulting in shorter matches. Also, the variants produce more balanced matches, maintaining the usual drama. We also identified limitations in the process, where, in many cases, where the objective function was correctly pursued, but the generated games were nearly trivial. This work paves the way towards promising research regarding the use of evolutionary game design beyond classic board games.

翻译：手工创建和评估游戏是一项艰巨且费力的任务。程序化内容生成可以通过创建游戏组件提供帮助，但通常无法生成完整的游戏。进化游戏设计结合了进化算法与自动化游戏测试，已用于创建装备简单的创新棋盘游戏；然而，原有方法并不涵盖包含骰子、卡牌和地图的复杂桌游。本研究提出了一种针对桌游的扩展方法，通过生成《Risk》的变体来评估该流程——这是一款军事策略游戏，玩家需征服地图上的领土以获胜。我们采用遗传算法进化所选参数，基于规则的智能体测试游戏，并利用多种质量标准评估生成的新变体。结果表明，我们成功创建了原游戏的新变体，其地图更小且游戏时长更短。此外，这些变体在保持原有戏剧性的同时，产生了更平衡的对局。我们也识别出流程中的局限性：在许多情况下，虽然目标函数被正确优化，但生成的游戏近乎无意义。这项工作为超越经典棋盘游戏的进化游戏设计研究开辟了前景。