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.
翻译:服务供应的迅速增长和多样化以及随之而来的信息技术生态系统的复杂性带来了许多管理挑战(业务和战略两方面),仪器和测量技术基本上跟上了这一发展和增长的步伐,然而,将数据转化为决策所需相关信息所需的算法、工具和技术却并非如此,本文(以及特邀演讲)的主张是,在人工智能不确定性方面进行的研究非常适合应对挑战和弥合这一差距,我将支持这一主张,并利用在诊断、模型发现和三个实际生活分布系统的政策优化方面的最新实例,讨论各种尚未解决的问题。