Embedding Normative Reasoning into Neural Symbolic Systems
Guido Boella, Silvano Colombo Tosatto, Artur d’Avila Garcez, Valerio Genovese, Leendert van der Torre · Open Repository and Bibliography (University of Luxembourg) · 2011
Normative systems are dynamic systems because their rules can change over time. Considering this problem, we propose a neuralsymbolic approach to provide agents the instruments to reason about and learn norms in a dynamic environment. We propose a variant of d’Avila Garcez et al. Connectionist Inductive Learning and Logic Programming(CILP) System to embed Input/Output logic normative rules into a feed-forward neural network. The resulting system called Normative-CILP(N-CILP) shows how neural networks can cope with some of the underpinnings of normative reasoning: permissions, dilemmas, exceptions and contrary to duty problems. We have applied our approach in a simplified RoboCup environment, using the N-CILP simulator that we have developed. In the concluding part of the paper, we provide some of the results obtained in the experiments. 1