Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks. (arXiv:2109.08078v2 [cs.AI] UPDATED)

Extracting spatial-temporal knowledge from data is useful in many
applications. It is important that the obtained knowledge is
human-interpretable and amenable to formal analysis. In this paper, we propose
a method that trains neural networks to learn spatial-temporal properties in
the form of weighted graph-based signal temporal logic (wGSTL) formulas. For
learning wGSTL formulas, we introduce a flexible wGSTL formula structure in
which the user's preference can be applied in the inferred wGSTL formulas. In
the proposed framework, each neuron …

ai arxiv graph graph-based networks neural networks signal