Implementation of a Transformation System for Relational Probabilistic Knowledge Bases Simplifying the Maximum Entropy Model Computation
نویسندگان
چکیده
The maximum entropy (ME) model of a knowledge base R consisting of relational probabilistic conditionals can be defined referring to the set of all ground instances of the conditionals. The logic FO-PCL employs the notion of parametric uniformity for avoiding the full grounding of R. We present an implementation of a rule system transforming R into a knowledge base that is parametrically uniform and has the same ME model, simplifying the ME model computation. The implementation provides different execution and evaluation modes, including the generation of all possible solutions.
منابع مشابه
Generation of Parametrically Uniform Knowledge Bases in a Relational Probabilistic Logic with Maximum Entropy Semantics
In a relational setting, the maximum entropy model of a set of probabilistic conditionals can be defined referring to the full set of ground instances of the conditionals. The logic FO-PCL uses the notion of parametric uniformity to ensure that the full grounding of the conditionals can be avoided, thereby greatly simplifying the maximum entropy model computation. In this paper, we describe a s...
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تاریخ انتشار 2014