  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. In order 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 the 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.
