CDI-Type II: Exploiting Collective Human Knowledge to Understand and Evolve Complex Networked Systems

CDI-Type II：利用人类集体知识来理解和发展复杂的网络系统

• 批准号: 0835652
• 负责人: Martin Rinard
• 金额: $ 145万
• 依托单位: Massachusetts Institute of Technology
• 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: 2008-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835652&HistoricalAwards=false
• 关键词: CDI; Type; II; Exploiting; Collective

Large networked computer systems are some of the most complex and sophisticated engineered systems ever constructed. Because of this complexity, our existing computing environment is unreliable, insecure, and difficult to use. One intriguing aspect of this situation is that most problems people encounter in practice are not new --- they have previously been seen and solved by others. Developing techniques that enable the automated analysis of this rich knowledge source is therefore a key step in advancing our understanding of this important class of engineered artifacts. The research effort focuses on developing techniques that automatically process existing repositories of human interactions regarding previously encountered problems and solutions to isolate potential solutions to the problem. It will then use multiple virtual machines to evaluate each of the potential solutions, then use this evaluation to automatically select an candidate.The expected benefits of this approach are three fold. First, developing methods that jointly analyze documents in natural language and formally structured computer information can lead to new insights and a deeper understanding of these important phenomena. Second, this research will yield a new class of techniques for reasoning about computer systems in the presence of uncertainty by leveraging collective knowledge of a user community. Finally, by automatically finding solutions to system problems, these methods hold out the promise of substantially reducing the cost and risks associated with using computers to perform many of the key activities in our society.

大型网络计算机系统是有史以来构造的最复杂和最精密的工程系统中的一些。由于这种复杂性，我们现有的计算环境是不可靠的，不安全的，难以使用。这种情况的一个有趣的方面是，人们在实践中遇到的大多数问题都不是新的-他们以前已经被别人看到并解决了。因此，开发能够自动分析这一丰富知识源的技术是推进我们对这一类重要工程工件理解的关键一步。 研究工作的重点是开发技术，自动处理现有的人类互动知识库，关于以前遇到的问题和解决方案，以隔离潜在的解决方案的问题。然后，它将使用多个虚拟机来评估每个潜在的解决方案，然后使用此评估来自动选择候选方案。首先，开发联合分析自然语言文档和正式结构化计算机信息的方法可以带来对这些重要现象的新见解和更深入的理解。 第二，这项研究将产生一类新的技术推理计算机系统中存在的不确定性，利用集体知识的用户社区。最后，通过自动找到系统问题的解决方案，这些方法有望大幅降低与使用计算机执行我们社会中许多关键活动相关的成本和风险。