HCC-Small: Web Games to Advance Interactive Learning Agents

机器学习（ML）提供了一种构建自适应系统的方法，同时避免了繁琐和脆弱的预编程；然而，机器学习技术不是为幼稚用户的输入而设计的，基本上仍然是专家为专家构建的工具。该项目通过考虑旨在向普通人学习的系统来重新构建机器学习研究问题，并提出：“机器如何更好地利用普通人能够提供的输入？”为此，项目活动包括以下内容：(1)构建一套简短的电脑游戏，包括训练互动角色，跨越各种ML算法和各种互动领域；(2)在网站上部署这些游戏，并收集各种技术在网络上向普通人学习的成功数据。这个交互式学习代理网站的目标是对人类与机器学习系统的交互产生广泛和原则性的理解。这一研究议程的成功将产生重大影响，导致交互式机器的发展，帮助我们完成日常生活中大大小小的任务。结果有可能为任何希望将学习和适应纳入最终用户应用程序的设计人员提供一个框架。此外，该研究有望在机器学习和人机交互研究社区之间架起一座桥梁，创造一种互利的伙伴关系。

Machine Learning (ML) promises a way to build adaptive systems while avoiding tedious and tenuous pre-programming; however, ML techniques are not designed for input from naive users, remaining by and large a tool built by experts for experts. This project reframes the ML research question by considering systems that are meant to learn from everyday people, asking: "how can machines take better advantage of the input that an everyday person is able to provide?" To this end, project activities include the following: (1) Building a suite of short computer games that involve training interactive characters, spanning a wide variety of ML algorithms and a wide variety of interaction domains and (2) deploying these games on a website, and collecting data on the success of various techniques in learning from the average person on the web.The goal is for this interactive learning agents website to yield a broad and principled understanding of human interaction with ML systems. The success of this research agenda will have a significant impact, leading to the development of interactive machines that assist us with the large and small tasks of our daily lives. Results have the potential to provide a framework for any designer who wishes to incorporate learning and adaptation into an end-user application. Further, the research promises to bridge the Machine Learning and Human-Comptuer Interaction research communities, creating a mutually beneficial partnership.