---
title: "Anytime Inference in Probabilistic Logic Programs with Tp-Compilation"
authors:
  - "Jonas Vlasselaer"
  - "Guy Van den Broeck"
  - "Angelika Kimmig"
  - "Wannes Meert"
  - "Luc De Raedt"
doi: ""
url: "https://ijcai.org/proceedings/2015/263"
pdf: "https://ijcai.org/proceedings/2015/0263.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "1852-1857"
track: "Main Track"
keywords:
---

# Anytime Inference in Probabilistic Logic Programs with Tp-Compilation

Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 1852-1857.

## Abstract

Existing techniques for inference in probabilistic logic programs are sequential: they first compute the relevant propositional formula for the query of interest, then compile it into a tractable target representation and finally, perform weighted model counting on the resulting representation. We propose Tp-compilation, a new inference technique based on forward reasoning. Tp-compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. Furthermore, an empirical evaluation shows that Tp-compilation effectively handles larger instances of complex real-world problems than current sequential approaches, both for exact and for anytime approximate inference.