Probabilistic Inference Modulo Theories
Abstract
We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers). While many solutions to probabilistic inference over logic representations have been proposed, SGDPLL(T) is simultaneously (1) lifted, (2) exact and (3) modulo theories, that is, parameterized by a background logic theory. This offers a foundation for extending it to rich logic languages such as data structures and relational data. By lifted, we mean algorithms with constant complexity in the domain size (the number of values that variables can take). We also detail a solver for summations with difference arithmetic and show experimental results from a scenario in which SGDPLL(T) is much faster than a state-of-the-art probabilistic solver.
Cite
@article{arxiv.1605.08367,
title = {Probabilistic Inference Modulo Theories},
author = {Rodrigo de Salvo Braz and Ciaran O'Reilly and Vibhav Gogate and Rina Dechter},
journal= {arXiv preprint arXiv:1605.08367},
year = {2016}
}
Comments
Submitted to StarAI-16 workshop as closely revised version of IJCAI-16 paper