A generic and symbolic model-based diagnostic reasoner with highly scalable properties

Modern computing technologies-hardware, software, and algorithmic-have enabled the deployment of more exacting diagnostic reasoning (DR) systems than has heretofore been possible. Compromises in algorithm and modeling paradigm complexity, due to computational throughput and state-space explosion constraints, have historically dominated practical applications of such systems. This paper describes approaches that have been shown to be applicable in a wide set of domains. The algorithms used are highly scaleable and support a symbolic modeling formalism for analyzing the properties of the complex, dynamic systems. Moreover, analysis of simultaneous failures occurs as a natural byproduct of this formalism