Parametric dependability analysis through probabilistic Horn abduction

• Authors: Andrea Bobbio;Stefania Montani;Luigi Portinale
• Venue: UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
• Year: 2002

Dependability modeling and evaluation is aimed at investigating that a system performs its function correctly in time. A usual way to achieve a high reliability is to design redundant systems that contain several replicas of the same subsystem. In order to provide compactness in system representation, parametric system modeling has been investigated in the jterature: a set of replicas of a given subsystem is parameterized so that only one representative instance is explicitly included in the model. While modeling aspects can be suitably addressed by these approaches, analytical tools working on parametric characterizations are often more difficult to be defined; the standard approach consists in "unfolding" the parametric model, in order to exploit standard analysis algorithms working at the unfolded "ground" level. In the present paper we consider the formalism of Parametric Fault Tree (PFT) and we show how it can be related to Probabilistic Horn Abduction (PHA). Since PHA is a framework where both modeling and analysis can be performed in a restricted firstorder language, we aim at showing that the conversion of a PFT into a PHA theory allows for an approach to dependability analysis directly exploiting parametric representation. We will show that classical qualitative and quantitative dependability measures can be characterized within PHA; this makes the PHA framework a candidate for PFT analysis, where also posterior probability computation (often neglected in standard Fault Tree analysis) can be naturally performed. A simple example of a multi-processor system with several replicated units is used to illustrate the approach.