Self-adaptation has been proposed as a mechanism to counter complexity in control problems of technical systems. A major driver behind self-adaptation is the idea to transfer traditional design-time decisions to runtime and into the responsibility of systems themselves. To deal with unforeseen events and conditions, systems need creativity—typically realized by means of machine learning capabilities. Such learning mechanisms are based on different sources of knowledge. Feedback from the environment used for reinforcement purposes is probably the most prominent one within the self-adapting and self-organizing (SASO) systems community. However, the impact of other (sub-)systems on the success of the individual system’s learning performance has mostly been neglected in this context. In this article, we propose a novel methodology to identify effects of actions performed by other systems in a shared environment on the utility achievement of an autonomous system. Consider smart cameras (SC) as illustrating example: For goals such as 3D reconstruction of objects, the most promising configuration of one SC in terms of pan/tilt/zoom parameters depends largely on the configuration of other SCs in the vicinity. Since such mutual influences cannot be pre-defined for dynamic systems, they have to be learned at runtime. Furthermore, they have to be taken into consideration when self-improving their own configuration decisions based on a feedback loop concept, e.g., known from the SASO domain or the Autonomic and Organic Computing initiatives. We define a methodology to detect such influences at runtime, present an approach to consider this information in a reinforcement learning technique, and analyze the behavior in artificial as well as real-world SASO system settings.

中文翻译：

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自适应行为的相互影响感知运行时学习

自适应已被提议作为一种机制来应对技术系统控制问题的复杂性。自适应背后的一个主要驱动力是将传统的设计时决策转移到运行时并由系统本身负责的想法。为了处理不可预见的事件和条件，系统需要创造力——通常通过机器学习能力来实现。这种学习机制基于不同的知识来源。用于强化目的的环境反馈可能是自适应和自组织 (SASO) 系统社区中最突出的反馈。然而，在这种情况下，其他（子）系统对单个系统学习绩效成功的影响大多被忽略了。在本文中，我们提出了一种新的方法来识别共享环境中其他系统执行的操作对自治系统的效用实现的影响。以智能相机 (SC) 为例：对于对象的 3D 重建等目标，一个 SC 在平移/倾斜/缩放参数方面最有希望的配置很大程度上取决于附近其他 SC 的配置。由于无法为动态系统预先定义这种相互影响，因此必须在运行时学习它们。此外，在基于反馈回路概念（例如，从 SASO 领域或自主和有机计算倡议中得知）自我改进自己的配置决策时，必须考虑它们。我们定义了一种在运行时检测此类影响的方法，