Translating between Horn Representations and their Characteristic Models
  Characteristic models are an alternative, model based, representation for
Horn expressions. It has been shown that these two representations are
incomparable and each has its advantages over the other. It is therefore
natural to ask what is the cost of translating, back and forth, between these
representations. Interestingly, the same translation questions arise in
database theory, where it has applications to the design of relational
databases. This paper studies the computational complexity of these problems.
Our main result is that the two translation problems are equivalent under
polynomial reductions, and that they are equivalent to the corresponding
decision problem. Namely, translating is equivalent to deciding whether a given
set of models is the set of characteristic models for a given Horn expression.
We also relate these problems to the hypergraph transversal problem, a well
known problem which is related to other applications in AI and for which no
polynomial time algorithm is known. It is shown that in general our translation
problems are at least as hard as the hypergraph transversal problem, and in a
special case they are equivalent to it.
