Improving the Efficiency of Approximate Inference for Probabilistic Logical Models by means of Program Specialization

by   Daan Fierens, et al.

We consider the task of performing probabilistic inference with probabilistic logical models. Many algorithms for approximate inference with such models are based on sampling. From a logic programming perspective, sampling boils down to repeatedly calling the same queries on a knowledge base composed of a static part and a dynamic part. The larger the static part, the more redundancy there is in these repeated calls. This is problematic since inefficient sampling yields poor approximations. We show how to apply logic program specialization to make sampling-based inference more efficient. We develop an algorithm that specializes the definitions of the query predicates with respect to the static part of the knowledge base. In experiments on real-world data we obtain speedups of up to an order of magnitude, and these speedups grow with the data-size.



There are no comments yet.


page 1

page 2

page 3

page 4


Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs (Technical Report)

State-of-the-art inference approaches in probabilistic logic programming...

Differentiable Learning of Logical Rules for Knowledge Base Reasoning

We study the problem of learning probabilistic first-order logical rules...

A Theory of Interactive Debugging of Knowledge Bases in Monotonic Logics

A broad variety of knowledge-based applications such as recommender, exp...

Knowledge Refactoring for Program Induction

Humans constantly restructure knowledge to use it more efficiently. Our ...

Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs

We introduce a novel sampling algorithm for Bayesian inference on impera...

A Generalizable Knowledge Framework for Semantic Indoor Mapping Based on Markov Logic Networks and Data Driven MCMC

In this paper, we propose a generalizable knowledge framework for data a...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.