HCC-Small: Web Games to Advance Interactive Learning Agents

HCC-Small：促进交互式学习代理的网页游戏

• 批准号: 0812116
• 负责人: Charles Isbell
• 金额: $ 45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812116&HistoricalAwards=false
• 关键词: HCC; Small; Web; Games; Advance

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.

机器学习（ML）承诺一种构建自适应系统的方法，同时避免繁琐和脆弱的预编程;然而，ML技术并不是为来自天真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日常人学习的系统来重新构建ML研究问题，并提出：“机器如何更好地利用日常人能够提供的输入？“为此，项目活动包括：（1）构建一套简短的计算机游戏，涉及训练交互式角色，跨越各种ML算法和各种交互领域，以及（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。目标是这种交互式学习代理网站，以产生对人类与ML系统交互的广泛而有原则的理解。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的大大小小的任务。 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师提供一个框架。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桥梁，建立互利的伙伴关系。