Clayton McMillan, Michael C. Mozer, Paul Smolensky
We describe a neural network, called RufeNet, that learns explicit, sym(cid:173) bolic condition-action rules in a formal string manipulation domain. RuleNet discovers functional categories over elements of the domain, and, at various points during learning, extracts rules that operate on these categories. The rules are then injected back into RuleNet and training continues, in a process called iterative projection. By incorpo(cid:173) rating rules in this way, RuleNet exhibits enhanced learning and gener(cid:173) alization performance over alternative neural net approaches. By integrating symbolic rule learning and subsymbolic category learning, RuleNet has capabilities that go beyond a purely symbolic system. We show how this architecture can be applied to the problem of case-role assignment in natural language processing, yielding a novel rule-based solution.