# Event-triggered Learning

## TL;DR

This paper introduces event-triggered learning, a method that adaptively updates models in event-triggered control systems to further reduce communication by detecting when the model no longer matches the system dynamics.

## Contribution

It proposes a novel event-triggered learning approach that monitors communication patterns to trigger model updates, enhancing efficiency and adaptability in control systems.

## Key findings

- Derived classes of learning triggers based on inter-communication times
- Proved effectiveness of triggers using concentration inequalities
- Demonstrated reduction in communication through theoretical analysis

## Abstract

The efficient exchange of information is an essential aspect of intelligent collective behavior. Event-triggered control and estimation achieve some efficiency by replacing continuous data exchange between agents with intermittent, or event-triggered communication. Typically, model-based predictions are used at times of no data transmission, and updates are sent only when the prediction error grows too large. The effectiveness in reducing communication thus strongly depends on the quality of the prediction model. In this article, we propose event-triggered learning as a novel concept to reduce communication even further and to also adapt to changing dynamics. By monitoring the actual communication rate and comparing it to the one that is induced by the model, we detect a mismatch between model and reality and trigger model learning when needed. Specifically, for linear Gaussian dynamics, we derive different classes of learning triggers solely based on a statistical analysis of inter-communication times and formally prove their effectiveness with the aid of concentration inequalities.