Artificial Neural Networks that Learn to Satisfy Logic Constraints

no code implementations • 8 Dec 2017 • Gadi Pinkas, Shimon Cohen

As a test case, block-world planning problems are used to train networks that learn to plan in that domain, but the techniques proposed could be used more generally as in integrating prior symbolic knowledge with statistical learning

Automated Theorem Proving

Neural-Symbolic Learning and Reasoning: A Survey and Interpretation

no code implementations • 10 Nov 2017 • Tarek R. Besold, Artur d'Avila Garcez, Sebastian Bader, Howard Bowman, Pedro Domingos, Pascal Hitzler, Kai-Uwe Kuehnberger, Luis C. Lamb, Daniel Lowd, Priscila Machado Vieira Lima, Leo de Penning, Gadi Pinkas, Hoifung Poon, Gerson Zaverucha

Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.

Philosophy