Neural-Symbolic integration has become a very active research
area in the last decade. In this paper, we present a new massively
parallel model for modal logic. We do so by extending the language of
Modal Prolog [32, 37] to allow modal operators in the head of the clauses.
We then use an ensemble of C-IL2P neural networks [14, 15] to encode
the extended modal theory (and its relations), and show that the ensemble
computes a fixpoint semantics of the extended theory. An immediate
result of our approach is the ability to perform learning from examples
efficiently using each network of the ensemble. Therefore, one can adapt
the extended C-IL2P system by training possible world representations.
Keywords: Neural-Symbolic Integration, Artificial Neural Networks,
Modal Logic, Change of Representation, Learning from Structured Data.