A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems
A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems
复制标题
自学习自治系统中主动知识构建的概念
DOI：
10.1109/fas-w.2018.00048
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Müller
中科院分区：
文献类型：
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作者：
Anthony Stein;Sven Tomforde;A. Diaconescu;J. Hähner;C. Müller
自我改进和自我集成系统（SISSY）的研究计划的出现是为了应对信息和通信技术急剧增加的复杂性。此类系统的自主在线学习能力已被认为是 SISSY 以及更广泛的自适应和自组织 (SASO) 系统领域的关键推动因素，因为它为处理非平稳环境的固有动态提供了技术基础，这些动态环境不断挑战这些系统带来不可预见的情况、干扰和不断变化的目标。然而，学习进度是由系统迄今为止所经历的情况的经验引导的——这种反应性学习策略自然会导致知识缺失或不适当。在本文中，我们定义了一个正式的系统模型，并为 SISSY 系统制定了一个抽象的学习任务。我们进一步引入知识和知识差距的概念，随后提出一个新颖的概念来自动评估系统的现有知识库，从而主动获取知识，为 SISSY/SASO 系统做好准备，以应对运行时发生的干扰和其他变化。通过提出的先验知识构建，我们追求的总体目标是提高自学习自主系统的鲁棒性和学习效率。赋予这些系统识别其知识库中未适当覆盖的区域的能力，增强其自我意识属性。
The research initiative of self-improving and self-integrating systems (SISSY) emerged as response to the dramatically increasing complexity in information and communication technology. Such systems' ability of autonomous online learning has been identified as a key enabler for SISSY as well as for the broader field of self-adaptive and self-organizing (SASO) systems, since it provides the technical basis for dealing with the inherent dynamics of non-stationary environments that continually challenge these systems with unforeseen situations, disturbances, and changing goals. However, the learning progress is guided by the experiences in terms of situations the system has been exposed to so far – this reactive learning strategy naturally results in missing or inappropriate knowledge. In this paper, we define a formal system model and formulate an abstract learning task for SISSY systems. We further introduce the notion of knowledge and knowledge gaps to subsequently present a novel concept to automatically assess a system's existing knowledge base and, consequently, to proactively acquire knowledge to prepare SISSY/SASO systems for coping with disturbances and other changes that occur at runtime. By the proposed a priori construction of knowledge, we pursue the overall goal to increase the robustness as well as the learning efficiency of self-learning autonomous systems. Endowing these systems with the ability of identifying regions in their knowledge base that are not appropriately covered, strengthens their self-awareness property.
一种具有局部性能测量的系统相互影响检测算法
DOI：
10.1109/saso.2015.23
发表时间：
2015
期刊：
2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems
影响因子：
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作者：
Stefan Rudolph;Sven Tomforde;Bernhard Sick;Jörg Hähner
通讯作者：
Jörg Hähner