An Instance-Based State Representation for Network Repair
Abstract
We describe a formal framework for diagnosis and re-pair problems that shares elements of the well known partially observable MDP and cost-sensitive classifica-tion models. Our cost-sensitive fault remediation model is amenable to implementation as a reinforcement-learning system, and we describe an instance-based state representation that is compatible with learning and planning in this framework. We demonstrate a system that uses these ideas to learn to efficiently restore net-work connectivity after a failure.
Cite
Text
Littman et al. "An Instance-Based State Representation for Network Repair." AAAI Conference on Artificial Intelligence, 2004.Markdown
[Littman et al. "An Instance-Based State Representation for Network Repair." AAAI Conference on Artificial Intelligence, 2004.](https://mlanthology.org/aaai/2004/littman2004aaai-instance/)BibTeX
@inproceedings{littman2004aaai-instance,
title = {{An Instance-Based State Representation for Network Repair}},
author = {Littman, Michael L. and Ravi, Nishkam and Fenson, Eitan and Howard, Richard E.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2004},
pages = {287-292},
url = {https://mlanthology.org/aaai/2004/littman2004aaai-instance/}
}