Coevolutionary automatic game content generation of physics and flighting style games

过程内容生成(PCG)表示自动创建用于计算机游戏的内容的自动过程。因此，它具有显著降低游戏开发成本的潜力。商业产品可用于自动生成可视内容。此外，在过去的五年中，有几个结果表明，在描述游戏中遇到的材料的结构布局方面，PCG取得了重大进展。这方面的例子包括电脑游戏冒险类游戏中所遇到的“迷宫般的世界”的自动生成，以及平台式游戏(如《超级马里奥兄弟》)或物理益智类游戏(如《割断绳子》)的特定内容实例。*在这项工作中，我们为两种游戏类型开发了PCG方法：1)一款愤怒的小鸟风格的游戏，目标是提供尽可能多样化的内容；2)格斗游戏，其中的内容是由能够抵抗玩家预测其移动的能力的好对手的可用性驱动的。我们为《愤怒的小鸟》物理风格的游戏定义PCG引擎的见解是定义解决方案轨迹，并使用它来确定构建内容的约束条件。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希望的内容。飞行游戏的PCG将利用共同进化过程来自动识别飞行代理策略的不同范围的策略。将对参数化约束进行调查，以鼓励发展不同的战斗风格。此外，我们还对扩展通常假定用于格斗游戏的成对战斗模式感兴趣，以便有几种同时的多对手战斗模式可用，从而将该类型扩展到成对游戏之外。

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.********