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Combining logic with probabilities is a core idea to uncertain reasoning. Recently, approaches to probabilistic conditional logics based on first-order languages have been proposed that employ the principle of maximum entropy (ME), e.g. the logic FO-PCL. In order to simplify the ME model computation, FO-PCL knowledge bases can be transformed so that they become parametrically uniform. On the other hand, conditional structures have been proposed as a structural tool for investigating properties of conditional knowledge bases. In this paper, we present a software system for the computation, visualization, and comparison of conditional structures for relational probabilistic knowledge bases as they evolve in the transformation process that achieves parametric uniformity.

probabilistic logic; conditional logic; first-order logic; maximum entropy; knowledge base; conditional structure; parametric uniformity