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.
This thesis presents genomes that develop SHRUTI representations of logical relations and episodic facts so that they are able to correctly answer questions ...
We propose a neural-symbolic approach to provide agents with the instruments to reason about and learn norms in a dynamic environment.
We propose a neural- symbolic approach to provide agents the instru- ments to reason about and learn norms in a dynamic environment.
Embedding Normative Reasoning into Neural Symbolic Systems. In Artur S. d'Avila Garcez, Pascal Hitzler, Luís C. Lamb, editors, Proceedings of the Seventh ...
In this paper we provide a neural-symbolic framework to model, reason about and learn norms in multi-agent systems. To this purpose, we define a fragment of ...
Aug 20, 2024 · Neurosymbolic AI is an approach that combines the strengths of neural networks (deep learning) with symbolic AI (rule-based reasoning)
Missing: Normative | Show results with:Normative
In this paper we provide a neural-symbolic framework to model, reason about and learn norms in multi-agent systems. To this purpose, we define a fragment of ...
Neural reasoning, also known as knowledge graph embedding, aims at learning the distributed embed- dings for entities and relations in KGs and infer- ring the ...
Neural reasoning, also known as knowledge graph embedding, aims at learning the distributed embeddings for entities and relations in KGs and inferring the ...
Missing: Normative | Show results with:Normative