Learning and Representing Temporal Knowledge in Recurrent Networks

@article{Borges2011LearningAR,
  title={Learning and Representing Temporal Knowledge in Recurrent Networks},
  author={Rafael V. Borges and Artur S. d'Avila Garcez and Lu{\'i}s C. Lamb},
  journal={IEEE Transactions on Neural Networks},
  year={2011},
  volume={22},
  pages={2409-2421}
}
The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing… CONTINUE READING

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