C4.5: programs for machine learning
C4.5: programs for machine learning
DEPEND: A Simulation-Based Environment for System Level Dependability Analysis
IEEE Transactions on Computers
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Machine Learning
Credible Uses of the Distributed Interactive Stimulation (DIS) System
Credible Uses of the Distributed Interactive Stimulation (DIS) System
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Defining and decomposing safety policy for systems of systems
SAFECOMP'05 Proceedings of the 24th international conference on Computer Safety, Reliability, and Security
Self-organisation for survival in complex computer architectures
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
The global financial markets: an ultra-large-scale systems perspective
Proceedings of the 17th Monterey conference on Large-Scale Complex IT Systems: development, operation and management
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In the operation of safety-critical systems, the sequences by which failures can lead to accidents can be many and complex. This is particularly true for the emerging class of systems known as systems of systems, as they are composed of many distributed, heterogenous and autonomous components. Performing hazard analysis on such systems is challenging, in part because it is difficult to know in advance which of the many observable or measurable features of the system are important for maintaining system safety. Hence there is a need for effective techniques to find causal relationships within these systems. This paper explores the use of machine learning techniques to extract potential causal relationships from simulation models. This is illustrated with a case study of a military system of systems.