|Title||Connectionist Model Generation: A First-Order Approach|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Sebastian Bader, Steffen Holldobler, Pascal Hitzler|
|Keywords||Connectionist Model Generation, First-Order Logic Programs, Neural-Symbolic Integration, Recurrent RBF Networks|
Knowledge based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes as expressed e.g., by means of first-order predicate logic, it is not obvious at all what neural symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core.We show in this paper how the core method can be used to learn first-order logic programs in a connectionist fashion, such that the trained network is able to do reasoning over the acquired knowledge. We also report on experimental evaluations which show the feasibility of our approach.
|Full Text|| |
Sebastian Bader, Pascal Hitzler, Steffen Hölldobler. 'Connectionist Model Generation: A First-Order Approach.' Neurocomputing Volume: 72, 2008: 2420-2432