Robustification of Online Graph Exploration Methods

TL;DR

This paper introduces a learning-augmented approach to online graph exploration, integrating machine-learned predictions into classical algorithms to improve performance while maintaining robustness against poor predictions.

Contribution

It proposes a novel algorithm that combines predictions with the Nearest Neighbor method, providing theoretical guarantees that adapt to prediction accuracy and extending this framework to general algorithms.

Findings

Significant performance improvements with high-quality predictions

Robust guarantees that degrade gracefully with prediction errors

Experimental validation supporting theoretical results

Abstract

Exploring unknown environments is a fundamental task in many domains, e.g., robot navigation, network security, and internet search. We initiate the study of a learning-augmented variant of the classical, notoriously hard online graph exploration problem by adding access to machine-learned predictions. We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm and significantly outperforms any known online algorithm if the prediction is of high accuracy while maintaining good guarantees when the prediction is of poor quality. We provide theoretical worst-case bounds that gracefully degrade with the prediction error, and we complement them by computational experiments that confirm our results. Further, we extend our concept to a general framework to robustify algorithms. By interpolating carefully between a given algorithm and NN, we prove new performance bounds that leverage the individual good performance on particular inputs while establishing robustness to arbitrary inputs.