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Graph Temporal Logic Inference for Classification and Identification by Xu, Zhe; Nettekoven, Alexander J; Julius, A. Agung; Topcu, Ufuk is a scholarly article available to read on EtoBox.

What is Graph Temporal Logic Inference for Classification and Identification about?

Inferring spatial-temporal properties from data is important for many complex systems, such as additive manufacturing systems, swarm robotic systems and biological networks. Such systems can often be modeled as a labeled graph where labels on the nodes and edges represent relevant measurements such as temperatures and distances. We introduce graph temporal logic (GTL) which can express properties such as "whenever a node's label is above 10, for the next 3 time units there are always at least two neighboring nodes with an edge label of at most 2 where the node labels are above 5". This paper is a first attempt to infer spatial (graph) temporal logic formulas from data for classification and identification. For classification, we infer a GTL formula that classifies two sets of graph temporal trajectories with minimal misclassification rate. For identification, we infer a GTL formula that is informative and is satisfied by the graph temporal trajectories in the dataset with high probability. The informativeness of a GTL formula is measured by the information gain with respect to given prior knowledge represented by a prior probability distribution. We implement the proposed approach

Author

Xu, Zhe; Nettekoven, Alexander J; Julius, A. Agung; Topcu, Ufuk

Published

2019

Language

EN