Time-Incremental Learning from Data Using Temporal Logics
- URL: http://arxiv.org/abs/2112.14300v1
- Date: Tue, 28 Dec 2021 21:32:00 GMT
- Title: Time-Incremental Learning from Data Using Temporal Logics
- Authors: Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile, and Calin Belta
- Abstract summary: We propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal.
We present a novel decision-tree based approach to generate a finite number of Signal Temporal Logic (STL) specifications from the given dataset.
The effectiveness and classification performance of our algorithm are evaluated on an urban-driving and a naval-surveillance case studies.
- Score: 3.167882687550935
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Real-time and human-interpretable decision-making in cyber-physical systems
is a significant but challenging task, which usually requires predictions of
possible future events from limited data. In this paper, we introduce a
time-incremental learning framework: given a dataset of labeled signal traces
with a common time horizon, we propose a method to predict the label of a
signal that is received incrementally over time, referred to as prefix signal.
Prefix signals are the signals that are being observed as they are generated,
and their time length is shorter than the common horizon of signals. We present
a novel decision-tree based approach to generate a finite number of Signal
Temporal Logic (STL) specifications from the given dataset, and construct a
predictor based on them. Each STL specification, as a binary classifier of
time-series data, captures the temporal properties of the dataset over time.
The predictor is constructed by assigning time-variant weights to the STL
formulas. The weights are learned by using neural networks, with the goal of
minimizing the misclassification rate for the prefix signals defined over the
given dataset. The learned predictor is used to predict the label of a prefix
signal, by computing the weighted sum of the robustness of the prefix signal
with respect to each STL formula. The effectiveness and classification
performance of our algorithm are evaluated on an urban-driving and a
naval-surveillance case studies.
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