Towards lifted inference under maximum entropy for probabilistic relational FO-PCL knowledge bases

Abstract

A knowledge base in the logic FO-PCL is a set of relational probabilistic conditionals. The models of such a knowledge base are probability distributions over possible worlds, and the principle of Maximum Entropy (ME) selects the unique model having maximum entropy. While previous work on FO-PCL focused on ME model computation, in this paper we propose two possible approaches towards lifted inference based on independent rule sets.