Distributing Knowledge into Simple Bases
  Understanding the behavior of belief change operators for fragments of
classical logic has received increasing interest over the last years. Results
in this direction are mainly concerned with adapting representation theorems.
However, fragment-driven belief change also leads to novel research questions.
In this paper we propose the concept of belief distribution, which can be
understood as the reverse task of merging. More specifically, we are interested
in the following question: given an arbitrary knowledge base $K$ and some
merging operator $\Delta$, can we find a profile $E$ and a constraint $\mu$,
both from a given fragment of classical logic, such that $\Delta_\mu(E)$ yields
a result equivalent to $K$? In other words, we are interested in seeing if $K$
can be distributed into knowledge bases of simpler structure, such that the
task of merging allows for a reconstruction of the original knowledge. Our
initial results show that merging based on drastic distance allows for an easy
distribution of knowledge, while the power of distribution for operators based
on Hamming distance relies heavily on the fragment of choice.
