Date: | 2021, April 13 |
Time: | 10:00 a. m. |
Place: | Online |
Author: | Mossakowski, Till |
Title: | Logical Neural Networks |
I will report about the paper “Logical neural networks” https://arxiv.org/abs/2006.13155 , using slides from the authors.
Abstract of the paper:
We propose a novel framework seamlessly providing key properties of both
neural nets (learning) and symbolic logic (knowledge and reasoning).
Every neuron has a meaning as a component of a formula in a weighted real-valued logic,
yielding a highly intepretable disentangled representation.
Inference is omnidirectional rather than focused on predefined target variables,
and corresponds to logical reasoning, including classical first-order logic
theorem proving as a special case.
The model is end-to-end differentiable, and learning minimizes a novel loss function
capturing logical contradiction, yielding resilience to inconsistent knowledge.
It also enables the open-world assumption by maintaining bounds on truth values
which can have probabilistic semantics, yielding resilience to incomplete knowledge.