DeepAI AI Chat
Log In Sign Up

Robustification of Online Graph Exploration Methods

by   Franziska Eberle, et al.
Universität Bremen

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.


page 1

page 2

page 3

page 4


Learning-Augmented Algorithms for Online Steiner Tree

This paper considers the recently popular beyond-worst-case algorithm an...

A Universal Error Measure for Input Predictions Applied to Online Graph Problems

We introduce a novel measure for quantifying the error in input predicti...

Double Coverage with Machine-Learned Advice

We study the fundamental online k-server problem in a learning-augmented...

Untrusted Predictions Improve Trustable Query Policies

We study how to utilize (possibly machine-learned) predictions in a mode...

Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions

A fundamental challenge for any intelligent system is prediction: given ...

Learning Augmented Energy Minimization via Speed Scaling

As power management has become a primary concern in modern data centers,...