1 research outputs found
Interval Logic Tensor Networks
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic
that interprets knowledge such as sequential properties (traces) and event
properties using sequences of real-featured data. We interpret connectives
using fuzzy logic, event durations using trapezoidal fuzzy intervals, and fuzzy
temporal relations using relationships between the intervals' areas. We propose
Interval Logic Tensor Networks (ILTN), a neuro-symbolic system that learns by
propagating gradients through IRL. In order to support effective learning, ILTN
defines smoothened versions of the fuzzy intervals and temporal relations of
IRL using softplus activations. We show that ILTN can successfully leverage
knowledge expressed in IRL in synthetic tasks that require reasoning about
events to predict their fuzzy durations. Our results show that the system is
capable of making events compliant with background temporal knowledge