Horn approximations of empirical data
نویسندگان
چکیده
منابع مشابه
Horn Approximations of Empirical Data
Formal AI systems traditionally represent knowledge using logical formulas. Sometimes, however, a model-based representation is more compact and enables faster reasoning than the corresponding formula-based representation. The central idea behind our work is to represent a large set of models by a subset of characteristic models. More speci cally, we examine model-based representations of Horn ...
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While the task of answering queries from an arbitrary propositional theory is intractable in general, it can typically be performed eeciently if the theory is Horn. This suggests that it may be more eecient to answer queries using a \Horn approximation"; i.e., a horn theory that is semantically similar to the original theory. The utility of any such approximation depends on how often it produce...
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We present a new approach to developing fast and efficient knowledge representation systems. Previous approaches to the problem of tractable inference have used restricted languages or incomplete inference mechanisms problems include lack of expressive power, lack of inferential power, and/or lack of a formal characterization of what can and cannot be inferred. To overcome these disadvantages, ...
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We present a new approach to developing fast and eecient knowledge representation systems. Previous approaches to the problem of tractable inference have used restricted languages or incomplete inference mechanisms | problems include lack of expressive power, lack of inferential power, and/or lack of a formal characterization of what can and cannot be inferred. To overcome these disadvantages, ...
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In a recent s tudy Selman and Kau tz proposed a me thod , called Horn approximation, for speeding up inference in proposi t ional Knowledge Bases. The i r technique is based on the compilation of a propos i t iona l fo rmu la in to a pair of H o r n formulae: a H o r n Greatest Lower Bound ( G L B ) and a Ho rn Least Upper Bound ( L U B ) . In this paper we address two questions tha t have been...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 1995
ISSN: 0004-3702
DOI: 10.1016/0004-3702(94)00072-9