Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Parametric dependability analysis through probabilistic Horn abduction
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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A novel configuration method for systems design has been developed, that considers, at the same time, system reliability and cost. This method helps to maximize the reliability, minimize the cost and obtain the best possible configuration for the system to be designed. To accomplish this, a combination of Bayesian networks and heuristic search are used so to help the designer find the optimum configuration in the immense search space available. The method has as entry parameters: the minimal reliability requirement or maximum cost of the computer system to be designed, the function of the system as a reliability block diagram and a description of each component. From this input, the methodology transforms automatically the reliability block diagram to Bayesian network equivalent, from which the reliability of the system is obtained through probability propagation. Starting form the initial block diagram, a set of heuristic operators is used to generate new configurations. The "best" configurations are obtained using beam search with some heuristics to improve the search efficiency. There are 3 alternatives for defining the best configurations: (i) minimize cost with a reliability restriction, (ii) maximize reliability with a cost restriction, and (iii) make a compromise between reliability and cost (Pareto set). The methodology is applied to the design of a distributed control system with promising results.