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Embedding Normative Reasoning into Neural Symbolic Systems
Boella, Guido; Colombo Tosatto, Silvano; d'Avila Garcez, Artur et al.
2011In Proceedings of the Seventh International Workshop on Neural-Symbolic Learning and Reasoning
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Abstract :
[en] 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
Disciplines :
Computer science
Author, co-author :
Boella, Guido
Colombo Tosatto, Silvano ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
d'Avila Garcez, Artur
Genovese, Valerio
van der Torre, Leon ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
yes
Language :
English
Title :
Embedding Normative Reasoning into Neural Symbolic Systems
Publication date :
2011
Event name :
7th Workshop on Neural-Symbolic Learning and Reasoning 2011, NeSy2011
Event date :
July 17, 2011
Audience :
International
Main work title :
Proceedings of the Seventh International Workshop on Neural-Symbolic Learning and Reasoning
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 12 December 2013

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