  Intelligent systems have the ability to improve their behaviour over time
taking observations, experiences or explicit feedback into account. Traditional
approaches separate the learning problem and make isolated use of techniques
from different field of machine learning such as reinforcement learning, active
learning, anomaly detection or transfer learning, for instance. In this
context, the fundamental reinforcement learning approaches come with several
drawbacks that hinder their application to real-world systems: trial-and-error,
purely reactive behaviour or isolated problem handling. The idea of this
article is to present a concept for alleviating these drawbacks by setting up a
research agenda towards what we call "active reinforcement learning" in
intelligent systems.
