@conference {17789,
	title = {Approximation results for probabilistic survivability},
	booktitle = {Multi-Agent Security and Survivability, 2005 IEEE 2nd Symposium on},
	year = {2005},
	month = {2005/08//},
	pages = {1 - 10},
	abstract = {As multiagent systems (MASs) are increasingly used in industrial applications, the need to make them more robust and resilient against disruption increases dramatically. The author has developed a probabilistic model (assuming complete ignorance of dependencies between node failures) of survivability based on deploying each agent in a MAS on one or more nodes. Finding a deployment that maximizes survivability is highly intractable for two reasons: firstly, computing the survivability of any deployment is intractable, and secondly, going through an exponential number of deployments to find the best one adds another layer of intractability. In this paper, we study what happens when node failures are independent. We show that computing survivability in this environment is still intractable. We propose various heuristics to compute the survivability of a given deployment. We have implemented and tested all these heuristics. We report on the advantages and disadvantages of different heuristics in different environmental settings.},
	keywords = {application;, computing;, failure;, fault, heuristic, heuristics, industrial, model;, multi-agent, multiagent, node, probabilistic, probability;, programming;, survivability;, system;, systems;, testing;, tolerant},
	doi = {10.1109/MASSUR.2005.1507042},
	author = {Zhang,Y. and Manister,E. and Kraus,S. and V.S. Subrahmanian}
}
@conference {16652,
	title = {Modeling diagnostic problem-solving at multiple levels of abstraction},
	booktitle = {Artificial Intelligence Applications, 1990., Sixth Conference on},
	year = {1990},
	month = {1990/05//},
	pages = {55 -61 vol.1 - 55 -61 vol.1},
	abstract = {A precise domain-independent computational model for diagnostic problem-solving at multiple levels of abstraction is presented. The knowledge representation framework allows causal knowledge to be represented in a precise yet natural way that reflects a human diagnostician{\textquoteright}s experience in guiding diagnostic reasoning at multiple levels of abstraction. The inference mechanism clearly defines how to form plausible diagnostic hypotheses guided by explicit causal links and the principle of parsimonious covering. It permits the efficient formation of high-level diagnostic hypotheses while at the same time ensuring that all plausible diagnostic alternatives will be considered if one wishes to reason at sufficiently detailed levels. A prototype implementation is described to demonstrate that the proposed inference model is natural for capturing a small but representative fragment of medical causal knowledge, and, with the addition of reasonable heuristics, that the inference mechanism exhibits desirable problem-solving behavior},
	keywords = {causal knowledge, diagnostic problem-solving, diagnostic reasoning, domain-independent computational model, explicit causal links, heuristic programming, heuristics, inference mechanism, inference mechanisms, knowledge capture, knowledge representation, knowledge representation framework, medical knowledge, multiple abstraction levels, parsimonious covering, plausible diagnostic hypotheses, problem solving},
	doi = {10.1109/CAIA.1990.89171},
	author = {Chu,B.-T.B. and Reggia, James A.}
}
