Self-aware computing systems: From psychology to engineering
Self-aware computing systems: From psychology to engineering
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自我意识计算系统：从心理学到工程学
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Peter R. Lewis
中科院分区：
文献类型：
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作者：
Peter R. Lewis
目前，在开发、部署和使用计算系统的方式上有几个根本性的变化。它们正变得越来越庞大、异质、不确定、动态和分散。这些复杂性导致运行时的行为难以理解或预测。如何应对这一挑战的一个愿景是赋予计算系统更多的自我意识，以实现先进的自主适应行为。在过去的二十年里，对自我意识的渴望已经出现在计算机科学和工程的各个领域，并且最近已经开发了对自我意识概念可能对计算系统的设计和操作意味着什么的更基本的理解。这借鉴了心理学和其他相关领域的自我意识理论，并在定义，架构，算法和案例研究方面做出了许多贡献。本文从心理学的角度介绍了自我意识的一些主要方面，这些方面已被用于开发计算中的相关概念。然后，它描述了这些概念已被翻译到计算域，并提供了如何明确的考虑可以导致系统更好地管理在运行时的背景下，一个复杂的环境中的冲突的目标之间的权衡的例子，同时减少需要在设计或部署时的先验域建模。
At the current time, there are several fundamental changes in the way computing systems are being developed, deployed and used. They are becoming increasingly large, heterogeneous, uncertain, dynamic and decentralised. These complexities lead to behaviours during run time that are difficult to understand or predict. One vision for how to rise to this challenge is to endow computing systems with increased self-awareness, in order to enable advanced autonomous adaptive behaviour. A desire for self-awareness has arisen in a variety of areas of computer science and engineering over the last two decades, and more recently a more fundamental understanding of what self-awareness concepts might mean for the design and operation of computing systems has been developed. This draws on self-awareness theories from psychology and other related fields, and has led to a number of contributions in terms of definitions, architectures, algorithms and case studies. This paper introduces some of the main aspects of self-awareness from psychology, that have been used in developing associated notions in computing. It then describes how these concepts have been translated to the computing domain, and provides examples of how their explicit consideration can lead to systems better able to manage trade-offs between conflicting goals at run time in the context of a complex environment, while reducing the need for a priori domain modelling at design or deployment time.