Efficient Datalog Abduction through Bounded Treewidth

Abductive diagnosis is an important method for identifying possible causes which explain a given set of observations. Unfortunately, abduction suffers from the fact that most of the algorithmic problems in this area are intractable. We have recently obtained very promising results for a strongly related problem in the database area. Specifically, the PRIMALITY problem becomes efficiently solvable and highly parallelizable if the underlying functional dependencies have bounded treewidth (Gottlob, Pichler, & Wei 2006b). In the current paper, we show that these favorable results can be carried over to logic-based abduction. In fact, we even show a further generalization of these results.

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