Algebraic Model Counting

Abstract

Weighted model counting (WMC) is a well-known inference task on knowledge bases, used for probabilistic inference in graphical models. We introduce algebraic model counting (AMC), a generalization of WMC to a semiring structure. We show that AMC generalizes many well-known tasks in a variety of domains such as probabilistic inference, soft constraints and network and database analysis. Furthermore, we investigate AMC from a knowledge compilation perspective and show that all AMC tasks can be evaluated using sd-DNNF circuits. We identify further characteristics of AMC instances that allow for the use of even more succinct circuits.

References in corpus (1)

• Inference in Probabilistic Logic Programs using Weighted CNF's

Cited by in corpus (2)

• On the Complexity of Optimization Problems based on Compiled NNF Representations
• Symmetric Weighted First-Order Model Counting