An instance-based state representation for network repair

Michael L. Littman; Nishkam Ravi; Eitan Fenson; Rich Howard

An instance-based state representation for network repair

Michael L. Littman, Nishkam Ravi, Eitan Fenson, Rich Howard

Research output: Contribution to conference › Paper › peer-review

Abstract

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 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.

Original language	English (US)
Pages	287-292
Number of pages	6
State	Published - 2004
Externally published	Yes
Event	Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) - San Jose, CA, United States Duration: Jul 25 2004 → Jul 29 2004

Other

Other	Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004)
Country/Territory	United States
City	San Jose, CA
Period	7/25/04 → 7/29/04

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Cite this

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title = "An instance-based state representation for network repair",

abstract = "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 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.",

author = "Littman, {Michael L.} and Nishkam Ravi and Eitan Fenson and Rich Howard",

year = "2004",

language = "English (US)",

pages = "287--292",

note = "Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) ; Conference date: 25-07-2004 Through 29-07-2004",

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An instance-based state representation for network repair. / Littman, Michael L.; Ravi, Nishkam; Fenson, Eitan et al.
2004. 287-292 Paper presented at Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004), San Jose, CA, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

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AU - Ravi, Nishkam

AU - Fenson, Eitan

AU - Howard, Rich

PY - 2004

Y1 - 2004

N2 - 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 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.

AB - 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 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.

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T2 - Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004)

Y2 - 25 July 2004 through 29 July 2004

ER -

An instance-based state representation for network repair

Abstract

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All Science Journal Classification (ASJC) codes

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