@conference {17811,
	title = {Probabilistically survivable mass},
	booktitle = {INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE},
	volume = {18},
	year = {2003},
	month = {2003///},
	pages = {789 - 795},
	abstract = {Multiagent systems (MAS) can "go down" for alarge number of reasons, ranging from system mal-
functions and power failures to malicious attacks.
The placement of agents on nodes is called a de-
ployment of the MAS. We develop a probabilis-
tic 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 de-
sired. 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.
},
	author = {Kraus,S. and V.S. Subrahmanian and Tas,N. C}
}
