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

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:
$145.0万
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
项目状态:
已结题
起止时间:
2008-09-01 至 2014-08-31

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中文摘要
翻译
大型联网计算机系统是有史以来最复杂和最复杂的工程系统之一。由于这种复杂性,我们现有的计算环境不可靠、不安全且难以使用。这种情况的一个耐人寻味的方面是,人们在实践中遇到的大多数问题都不是新的-这些问题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的知识来源的技术是推进我们对这类重要的工程构件的理解的关键一步。研究工作的重点是开发技术,自动处理关于以前遇到的问题和解决方案的现有人工交互存储库,以分离出问题的潜在解决方案。然后,它将使用多个虚拟机来评估每个潜在的解决方案,然后使用此评估自动选择候选解决方案。此方法的预期好处有三个。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新的见解和对这些重要现象的更深层次的理解。其次,这项研究将产生一种新的技术,通过利用用户社区的集体知识,在存在不确定性的情况下对计算机系统进行推理。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的许多关键活动相关的成本和风险。
英文摘要
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.
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会议论文
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