Coevolutionary automatic game content generation of physics and flighting style games

程序内容生成（PCG）表示自动创建计算机游戏内容的自动过程。因此，它有可能大大降低游戏开发的成本。商业产品可用于自动生成视觉内容。此外，在过去的五年中，PCG在描述游戏中遇到的材料的结构布局方面取得了重大进展。示例包括在冒险类型的计算机游戏中遇到的“迷宫般的世界”的自动生成，以及用于平台风格游戏（诸如“超级马里奥兄弟”）或物理益智风格游戏（诸如“割绳子”）的内容的特定实例。在这项工作中，我们开发了两种游戏类型的PCG方法：1）愤怒的小鸟风格的游戏，其目标是提供尽可能多样化的内容，以及2）战斗游戏，其中内容由能够抵抗玩家预测其移动的能力的优秀对手的可用性驱动。我们为愤怒的小鸟物理风格游戏定义PCG引擎的洞察力是定义解决方案轨迹，并使用它来识别构建内容的约束。内容的变化将来自于假设内容生成的随机过程，而约束的使用有助于指导变化操作员有效地识别最有前途的内容。飞行游戏的PCG将利用协同进化过程自动识别飞行代理策略的各种策略。将研究参数化限制，以鼓励发展不同的战斗风格。此外，我们也有兴趣扩展通常假设为格斗游戏的成对战斗模型，使几个同时多对手模式的战斗是可用的，从而扩展类型超越成对发挥。

Procedural content generation (PCG) represents an automatic process by which content for computer games is automatically created. As such, it has the potential to significantly reduce the cost of game development. Commercial products are available for automatically generating visual content. Moreover, there have been several results over the last five years in which significant advances have been made to PCG for describing the structural layout of material encountered in a game. Examples include the automatic generation of 'maze like worlds' as encountered in the adventure genre of computer games, and specific instances of content for platform style games - such as 'Super Mario Bros' - or physics puzzle style games such as 'Cut the Rope'. In this work, we develop PCG approaches for two gaming genres: 1) an Angry Birds style game in which the goal is to provide as diverse a range of content as possible, and 2) fighting games in which content is driven by the availability of good opponents that are able to resist the player's ability to predict their moves. Our insight for defining the PCG engine for the Angry Birds physics style game is to define solution trajectories and use this to identify constraints from which content is constructed. Variation in content will come from assuming a stochastic process for content generation, whereas the use of constraints helps guide variation operators to efficiently identify the most promising content. PCG for flighting games will make use of coevolutionary processes for automatically identifying a diverse range of strategies for flighting agent strategies. Parameterization constraints will be investigated to encourage the development of distinct fighting styles. Moreover, we are also interested in extending the pairwise model of combat typically assumed for fighting games so that several simultaneous multi-opponent modes of combat are available, thus extending the genre beyond pairwise play.