Embedding Normative Reasoning into Neural Symbolic Systems

Normative systems are dynamic systems because their rules can change over time. Considering this problem, we propose a neural- symbolic approach to provide agents the instru- ments to reason about and learn norms in a dynamic environment. We propose a variant of d’Avila Garcez et al. Con- nectionist Inductive Learning and Logic Program- ming(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 simula- tor that we have developed. In the concluding part of the paper, we provide some of the results ob- tained in the experiments