  In this paper we present a computational modeling account of an active self
in artificial agents. In particular we focus on how an agent can be equipped
with a sense of control and how it arises in autonomous situated action and, in
turn, influences action control. We argue that this requires laying out an
embodied cognitive model that combines bottom-up processes (sensorimotor
learning and fine-grained adaptation of control) with top-down processes
(cognitive processes for strategy selection and decision-making). We present
such a conceptual computational architecture based on principles of predictive
processing and free energy minimization. Using this general model, we describe
how a sense of control can form across the levels of a control hierarchy and
how this can support action control in an unpredictable environment. We present
an implementation of this model as well as first evaluations in a simulated
task scenario, in which an autonomous agent has to cope with un-/predictable
situations and experiences corresponding sense of control. We explore different
model parameter settings that lead to different ways of combining low-level and
high-level action control. The results show the importance of appropriately
weighting information in situations where the need for low/high-level action
control varies and they demonstrate how the sense of control can facilitate
this.
