Inferential Complexity Control for Model-Based Abduction

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Inferential Complexity Control for Model-Based Abduction
We describe a technique for speeding up inference for model-based abduction tasks that trades off inference time and/or space for the fraction of queries correctly answered. We compile a knowledge base (for which inference may be intractable) into a set of rules that cover the most likely queries using simple criteria that do not entail extensive knowledge engineering effort, such as subset-minimal or most probable query-responses. We demonstrate this approach on the abduction task of model-based diagnosis, and show that this approach can predictably produce order-of-magnitude reductions in time and memory requirements for abductive tasks in which the queries have skewed distributions; for example, in diagnosis the faults are skewed towards being highly unlikely.
Gregory M. Provan
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where KR
Authors Gregory M. Provan
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