Inference in probabilistic logic languages such as ProbLog, an extension of Prolog with probabilistic facts, is often based on a reduc- tion to a propositional formula in DNF. Calculating the probability of such a formula involves the disjoint-sum-problem, which is computation- ally hard. In this work we introduce a new approximation method for ProbLog inference which exploits the DNF to focus sampling. While this DNF sampling technique has been applied to a variety of tasks before, to the best of our knowledge it has not been used for inference in probabilis- tic logic systems. The paper also presents an experimental comparison with another sampling based inference method previously introduced for ProbLog.
|Number of pages||1|
|Publication status||Published - 2010|
|Event||26th International Conference on Logic Programming, ICLP 2010 - Joint Workshop on Implementation of Constraint Logic Programming Systems and Logic-based Methods in Programming Environments - CICLOPS-WLPE 2010 - Edinburgh, Scotland, United Kingdom|
Duration: 15 Jul 2010 → 15 Jul 2010
|Conference||26th International Conference on Logic Programming, ICLP 2010 - Joint Workshop on Implementation of Constraint Logic Programming Systems and Logic-based Methods in Programming Environments - CICLOPS-WLPE 2010|
|Period||15/07/10 → 15/07/10|