.png){kind=icon}
.png){kind=icon}
Return
Tp-Compilation for inference in probabilistic logic programs
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2016.06.009.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose Tp-compilation, a new inference technique for probabilistic logic programs that is 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. The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations. Typically, these transformations include conversion of the ground program into an equivalent propositional formula and compilation of this formula into a more tractable target representation for weighted model counting. An empirical evaluation shows that Tp-compilation effectively handles larger instances of complex or cyclic real-world problems than current sequential approaches, both for exact and anytime approximate inference. Furthermore, we show that Tp-compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Probabilistic inference
Knowledge compilation
Probabilistic logic programs
Dynamic relational models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.9K
Citations:
5.1K
