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Hierarchical Model-Based Diagnosis based on Structural Abstraction
Authors: Chittaro L., Ranon R.
Published in: Artificial Intelligence, Vol. 155, Issues 1-2, 2004, pp.147-182
Abstract: Abstraction has been advocated as one of the main remedies fo the computational complexity of model-based diagnosis. However, after the seminal work published in the early nineties, littel research has been devoted to this topic. In this paper, we consider one of the types of abstractio, investigating it both from a theoretical and practical point of view. First, we provide a new formalization for structural abstraction commonly used in diagnosis, i.e. structural abstraction, investigating it both from a theoretical and practical point of view. First, we provide a new formalization for structural abstraction that generalizes and extends previous ones. Then, we present two new different techniques for model-based diagnosis that automatically derive easier-to-diagnose versions of a (hierarchical) diagnosis problem on the basis of the available observations. The two proposed techniques are formulated as extensions of the well-known Mozetic.s algorithm, and experimentally contrasted with it to evaluate the obtained efficiency gains.