2011/12/22 by Daan Fierens, Fierens, Daan
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #D.1.6 #FOS: Computer and information sciences #G.3 #I.2.2 #Machine Learning and Algorithms #cs.AI
paper · pdf · doi:10.48550/arxiv.1112.5381
17 pages
arxiv created 2011/12/22 · openalex publication_date 2011/12/22 · arxiv updated 2015/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.