Diagnosis as Semiring-Based Constraint Optimization

Constraint optimization is at the core of many problems in Artificial Intelligence. In this paper, we frame model-based diagnosis as a constraint optimization problem over lattices. We then show how it can be captured in a framework for "soft" constraints known as semiring-CSPs. The well-defined mathematical properties of a semiring-CSP permit us to devise efficient solution methods based on decomposing diagnostic problems into trees and applying dynamic programming. We relate the approach to SAB and TREE*, two diagnosis algorithms for tree-structured systems, which correspond to special cases of semiring-based constraint optimization.

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