AAAI Publications, Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning

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Knowledge Compilation for Lifted Probabilistic Inference: Compiling to a Low-Level Language
Seyed Mehran Kazemi, David Poole

Last modified: 2016-03-30

Abstract


Algorithms based on first-order knowledge compilation are currently the state-of-the-art for lifted inference. These algorithms typically compile a probabilistic relational model into an intermediate data structure and use it to answer many inference queries. In this paper, we propose compiling a probabilistic relational model directly into a low-level target (e.g., C or C++) program instead of an intermediate data structure and taking advantage of advances in program compilation. Our experiments represent orders of magnitude speedup compared to existing approaches.

Keywords


Exact Inference; Graphical Models; Statistical Relational Artificial Intelligence; Lifted Inference; First-Order Knowledge Compilation

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