SoCS: Creation of a Framework for Computational Gaming

利用众包来收集数据正变得越来越普遍。Human Computation尤其关注如何使用人工智能处理从玩家身上收集到的数据，以验证大规模收集到的有用数据。这项工作将研究一种新的基于人工智能的众包游戏，称为计算游戏，在这种游戏中，问题将在事先不知道答案的情况下提出。需要人类判断的问题将在游戏环境中提出，机器学习将用于确定向哪些玩家提出什么问题，以及如何确定回答是否有效。知识价值。该项目将通过文本和图像标记的两个例子来证明计算游戏的有效性，描绘一套用于构建和评估未来计算游戏设计的指导性设计原则，并制作一个工具包，支持和鼓励使用这些设计原则来构建计算游戏系统。潜在的更广泛影响。该项目将更快、更便宜地创建人工标记数据的数据库；它还将在比目前存在的更广泛的问题上这样做。计算游戏的框架和工具包对于游戏设计师、需要标记数据的许多领域的研究人员以及正在进行研究的用户来说都是有价值的。

Leveraging crowdsourcing to collect data is becoming more common. Human Computation, in particular, has looked at how to use artificial intelligence on data collected from people playing games, to validate that useful data has been collected on a very large scale. This work will investigate a new form of artificial-intelligence based crowdsourced games called Computational Gaming, in which questions will be posed without knowing what the answers are beforehand. Questions that require human judgment will be posed in the context of a game, and machine learning will be used to determine what questions to pose to which players and how to determine whether the responses are valid.Intellectual Merit. This project will demonstrate the validity of Computational Gaming through two examples in text and image labeling, delineating a set of guiding design principles for building and evaluating future Computational Gaming designs, and producing a toolkit that supports and encourages the use of these design principles for building Computational Gaming systems.Potential Broader Impacts. The project will create, both more quickly and more cheaply, databases of human-labeled data; it will also do so for a wider variety of problems than currently exists. The framework and toolkit for Computational Gaming will be valuable for game designers, for researchers in many domains that need labeled data, and for the users for whom the research is being conducted.