Probabilistically survivable MASs

Sarit Kraus, V. S. Subrahmanian, N. Cihan Tas

Research output: Contribution to journalConference articlepeer-review

21 Scopus citations


Muhiagent systems (MAS) can "go down" for a large number of reasons, ranging from system malfunctions and power failures to malicious attacks. The placement of agents on nodes is called a deployment of the MAS. We develop a probabilistic model of survivability of a deployed MAS and provide two algorithms to compute the probability of survival of a deployed MAS. Our probabilistic model docs not make independence assumptions though such assumptions can be added if so desired. An optimal deployment of a MAS is one that maximizes its survival probability. We provide a mathematical answerto this question, an algorithm that computes an exact solution to this problem, as well as several algorithms that quickly compute approximate solutions to the problem. We have implemented our algorithms - our implementation demonstrates that computing deployments can be done scalably.

Original languageEnglish
Pages (from-to)789-795
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2003
Event18th International Joint Conference on Artificial Intelligence, IJCAI 2003 - Acapulco, Mexico
Duration: 9 Aug 200315 Aug 2003


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