Eberle, Franziska, Lindermayr, Alexander, Megow, Nicole, Nölke, Lukas and Schlöter, Jens (2022) Robustification of online graph exploration methods. In: AAAI-22 Technical Tracks 9. Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (9). Association for the Advancement of Artificial Intelligence, pp. 9732-9740. ISBN 1577358767
Full text not available from this repository.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.
| Item Type: | Book Section |
|---|---|
| Official URL: | https://aaai.org/papers/09732-robustification-of-o... |
| Additional Information: | © 2022, Association for the Advancement of Artificial Intelligence |
| Divisions: | Mathematics |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Date Deposited: | 19 Sep 2024 10:09 |
| Last Modified: | 28 Nov 2024 22:36 |
| URI: | http://eprints.lse.ac.uk/id/eprint/125461 |
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