Corpus ID: 51059602

Report from Dagstuhl Seminar 17192 Human-Like Neural-Symbolic Computing

  title={Report from Dagstuhl Seminar 17192 Human-Like Neural-Symbolic Computing},
  author={Tarek R. Besold and A. Garcez and L. Lamb},
This report documents the program and the outcomes of Dagstuhl Seminar 17192 “Human-Like Neural-Symbolic Computing”, held from May 7th to 12th, 2017. The underlying idea of HumanLike Computing is to incorporate into Computer Science aspects of how humans learn, reason and compute. Whilst recognising the relevant scientific trends in big data and deep learning, capable of achieving state-of-the-art performance in speech recognition and computer vision tasks, limited progress has been made… CONTINUE READING


Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)
  • 8
  • PDF
Perspectives of Neural-Symbolic Integration
  • 84
Neural-Symbolic Cognitive Reasoning
  • 122
  • PDF
Neural-Symbolic Learning and Reasoning: Contributions and Challenges
  • 97
  • PDF
Learning and Representing Temporal Knowledge in Recurrent Networks
  • 37
  • PDF
A Connectionist Computational Model for Epistemic and Temporal Reasoning
  • 24
Connectionist modal logic: Representing modalities in neural networks
  • 40
  • PDF
Learning of control in a neural architecture of grounded language processing
  • 25
Evolving Culture vs Local Minima
  • 24
  • PDF