Experimental and causal view on information integration in autonomous
  agents
  The amount of digitally available but heterogeneous information about the
world is remarkable, and new technologies such as self-driving cars, smart
homes, or the internet of things may further increase it. In this paper we
present preliminary ideas about certain aspects of the problem of how such
heterogeneous information can be harnessed by autonomous agents. After
discussing potentials and limitations of some existing approaches, we
investigate how \emph{experiments} can help to obtain a better understanding of
the problem. Specifically, we present a simple agent that integrates video data
from a different agent, and implement and evaluate a version of it on the novel
experimentation platform \emph{Malmo}. The focus of a second investigation is
on how information about the hardware of different agents, the agents' sensory
data, and \emph{causal} information can be utilized for knowledge transfer
between agents and subsequently more data-efficient decision making. Finally,
we discuss potential future steps w.r.t.\ theory and experimentation, and
formulate open questions.
