References of "d'Avila Garcez, Artur"
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See detailEmbedding Normative Reasoning into Neural Symbolic Systems
Boella, Guido; Colombo Tosatto, Silvano UL; d'Avila Garcez, Artur et al

in Proceedings of the Seventh International Workshop on Neural-Symbolic Learning and Reasoning (2011)

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 ... [more ▼]

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 [less ▲]

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See detailNeural Symbolic Architecture for Normative Agents
Boella, Guido; Colombo Tosatto, Silvano UL; d'Avila Garcez, Artur et al

in In Proceedings of The Seventh International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011). May, 2011 (2011)

In this paper we propose a neural-symbolic architecture to represent and reason with norms in multi-agent systems. On the one hand, the architecture contains a symbolic knowledge base to represent norms ... [more ▼]

In this paper we propose a neural-symbolic architecture to represent and reason with norms in multi-agent systems. On the one hand, the architecture contains a symbolic knowledge base to represent norms and on the other hand it contains a neural network to reason with norms. The interaction between the symbolic knowledge and the neural network is used to learn norms. We describe how to handle normative reasoning issues like contrary to duties, dilemmas and exceptions by using a priority-based ordering between the norms in a neural-symbolic architecture. [less ▲]

Detailed reference viewed: 31 (1 UL)