CAD-GAME: Computer-Aided Game Design

作为一种媒体形式——越来越多的大众媒体——游戏与电影有很多共同之处，尤其是在商业模式方面。电影高风险的一个原因是，在产品发行之前，只能进行有限的市场测试，从那时起，产品基本上是固定的。游戏传统上遵循这一模式，但重要的是要记住，游戏不仅仅是媒体——它们也是软件。来自软件行业的一个主要经验是，捕获用户与产品的交互可以通过几种方式用于改进市场产品：通过在发布后进行调整（例如自适应界面），通过使用该数据在未来的版本中迭代地改进产品（例如微软和苹果的错误报告），以及更深入地了解不同的用户组。从本质上讲，我们认为这种方法可以直接应用于游戏。然而，困难在于我们不能使用简单的任务完成指标，因为根本就不存在任务：我们只是在尝试娱乐。主要的研究挑战是更好地理解“娱乐”这个词在游戏中的真正含义，以及它如何反映在玩家的行为模式中。为此，我们将对电子游戏中的乐趣和沉浸感进行研究，以便得出一种具体的方法，在游戏的相当细粒度间隔内，精确估计玩家的乐趣和沉浸感。我们还将从实际玩Rebellion游戏的玩家那里获取大量不同形式的数据，我们将使用人工智能的先进技术来搜索与玩家整体享受的相关性。这些技巧可以在设计过程中用于加速调整各种游戏参数的过程——敌人在哪里，武器有多有效等等。更有趣的是，数据分析工具可以在游戏中运行，根据玩家的行为调整游戏。通过这种方式，我们希望开创一个用户自适应电子游戏的新时代，在一款游戏中，有许多玩法范例，也就是说，每个玩家都有一个独特的游戏体验，这是根据他们的个性、经验水平、游戏风格和情绪量身定制的。

As a form of media - increasingly, mass media - games have much in common with film, particularly in terms of business models. One reason for the high levels of risk in film is that only limited market testing can be done prior to product release, and the product is essentially fixed from then on. Games have traditionally followed this model, but it is important to remember that games are not just media - they are also pieces of software. One major lesson from the software industry is that capturing users' interactions with a product can be used to refine market offerings in several ways: by adapting it after release (e.g. adaptive interfaces), by using that data to iteratively refine products in future releases (e.g. error reporting in Microsoft and Apple), and to understand the various user groups in more depth. In essence, we are suggesting that this approach can be directly applied to games. The difficulty, however, is that we cannot use simple measures of task completion, because there is no task: we are simply trying to entertain. The main research challenge is to understand better just what the word 'entertain' really means in games, and how this might be reflected in patterns of player behaviour. To do this, we will undertake a study of enjoyment and immersion in video games, in order to derive a concrete methodology for closely estimating the amount of enjoyment and immersion a player has, at fairly fine-grained intervals of a game. We will also capture a multitude of different forms of data from players actually playing Rebellion's games, and we will use advanced techniques from Artificial Intelligence to search for correlations with the player's overall enjoyment. These techniques could be used during design to speed up the process of tweaking the various game parameters - where the enemies are, how effective weapons are, etc. More interesting, however, is the idea that data analysis tools could run within the game itself, adapting the game in response to the player's behaviour. In this way, we hope to pioneer a new age of user-adaptive video games, within a one game, many gameplays paradigm, i.e., where each player has a unique game experience which is tailored to their personality, level of experience, playing style and mood.