An Instance-Based State Representation for Network Repair

We describe a formal framework for diagnosis and repair problems that shares elements of the well known partially observable MDP and cost-sensitive classification models. Our cost-sensitive fault remediation model is amenable to implementation as a reinforcementlearning 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.

• Type: Conference
• Year: 2004
• Where: AAAI
• Authors: Michael L. Littman, Nishkam Ravi, Eitan Fenson, Rich Howard
• Keywords: AAAI 2004, Cost-sensitive Classification Models, Cost-sensitive Fault Remediation, Intelligent Agents, Observable Mdp