Inspired by “GoogleTM Sets” and Bayesian sets, we consider the problem of retrieving complex objects and relations among them, i.e., ground atoms from a logical concept, given...
We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples,...
Querying any information system requires the knowledge of some formal language, making it inaccessible to computer-na?ve potential users. We propose a new intuitive querying mecha...
We introduce an extension of linear constraints, called linearrange constraints, which allows for (meta-)reasoning about the approximation width of variables. Semantics for linear...
A concept learning framework for terminological representations is introduced. It is grounded on a method for inducing logic decision trees as an adaptation of the classic tree in...