An instance-based state representation for network repair

Authors:
Michael L. Littman;Nishkam Ravi;Eitan Fenson;Rich Howard
Affiliations:
Department of Computer Science, Rutgers University, Piscataway, NJ;Department of Computer Science, Rutgers University, Piscataway, NJ;PnP Networks, Inc., Los Altos, CA;PnP Networks, Inc., Los Altos, CA
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

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Abstract

We describe a formal framework for diagnosis and repair problems that shares elements of the well known partially observable MOP and cost-sensitive classification 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 network connectivity after a failure.