Robustification of Online Graph Exploration Methods
This addresses the challenge of exploring unknown environments in domains like robotics and network security, offering a robust, learning-augmented approach that is incremental over existing methods.
The paper tackles the online graph exploration problem by integrating machine-learned predictions into the Nearest Neighbor algorithm, achieving significant performance improvements with high-accuracy predictions and maintaining robustness with poor predictions, supported by theoretical bounds and computational experiments.
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.