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