%0 Conference Paper %B INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE %D 2003 %T Probabilistically survivable mass %A Kraus,S. %A V.S. Subrahmanian %A Tas,N. C %X 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. %B INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE %V 18 %P 789 - 795 %8 2003/// %G eng