一次一个大系统让生活更美好：UAI面临的挑战 研究

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摘要翻译：
服务提供的迅速增长和多样性以及随之而来的信息技术生态系统的复杂性提出了许多管理挑战（包括业务和战略）。仪器和测量技术大体上跟上了这一发展和增长的步伐。然而，将数据转换为决策所需的相关信息所需的算法、工具和技术却不是。本文（和受邀演讲）中的主张是，在人工智能的不确定性方面进行的研究非常适合解决这些挑战并缩小这一差距。我将支持这一主张，并使用最近在三个实际的分布式系统上的诊断、模型发现和策略优化方面的例子来讨论开放的问题。
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英文标题：
《Making life better one large system at a time: Challenges for UAI
  research》
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作者：
Moises Goldszmidt
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Software Engineering        软件工程
分类描述：Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
涵盖设计工具、软件度量、测试和调试、编程环境等。大致包括ACM所有主题课程D.2的材料，除了D.2.4（程序验证）可能应该有计算机科学中的逻辑作为主要主题领域。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The rapid growth and diversity in service offerings and the ensuing complexity of information technology ecosystems present numerous management challenges (both operational and strategic). Instrumentation and measurement technology is, by and large, keeping pace with this development and growth. However, the algorithms, tools, and technology required to transform the data into relevant information for decision making are not. The claim in this paper (and the invited talk) is that the line of research conducted in Uncertainty in Artificial Intelligence is very well suited to address the challenges and close this gap. I will support this claim and discuss open problems using recent examples in diagnosis, model discovery, and policy optimization on three real life distributed systems.
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PDF链接：
https://arxiv.org/pdf/1206.5279

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关键词：大系统 Intelligence Environments Verification Optimization 讨论 所需 挑战 technology 发现