Reasoning about Time and Knowledge in Neural Symbolic Learning Systems

38 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, background): “In order to illustrate the computational power of Connectionist Modal Logics (CML) and Connectionist Temporal Logics of Knowledge (CTLK) [8, 9], researchers were able to learn full solutions to several problems in distributed, multiagent learning and reasoning, including the Muddy Children Puzzle…”
Neurosymbolic AI: the 3rd wave (2020, background): “Foundational work on neural-symbolic integration and neurosymbolic AI models and systems (d’Avila Garcez et al. 2002, 2009; d’Avila Garcez and Lamb 2003; Hammer and Hit-zler 2007), discussed next, will be relevant as we embark in the journey to seek to answer the above research question.”
A Connectionist Computational Model for Epistemic and Temporal Reasoning (2006, background): “…d’Avila Garcez, Lamb, Broda, and Gabbay (2003) and d’Avila Garcez et al. (2004), we show that temporal and epistemic logics, by means of temporal and epistemic logic programming fragments, can be effectively represented in, and combined with, artificial neural networks (d’Avila Garcez & Lamb 2004).”