Selman and Kautz proposed a method, called Horn approximation, for speeding
up inference in propositional Knowledge Bases. Their technique is based on
the compilation of a propositional formula into a pair of Horn formulae: a
Horn Greatest Lower Bound (GLB) and a Horn Least Upper Bound (LUB). In thi
s paper we focus on GLBs and address two questions that have been only marg
inally addressed so far:
(1) what is the semantics of the Horn GLBs?
(2) what is the exact complexity of finding them?
We obtain semantical as well as computational results. The major semantical
result is: The set of minimal models of a propositional formula and the se
t of minimum models of its Horn GLBs are the same. The major computational
result is: Finding a Horn GLB of a propositional formula in CNF is NP-equiv
alent, (C) 2000 Elsevier Science B.V. All rights reserved.